How hateful rhetoric connects to real-world violence |
- Harnessing the Infrastructure Investment and Jobs Act to train the next generation of workers
- How does the Fed define “maximum employment”?
- The Summit for Democracy commitments are out—now what?
- Educating children to make the invisible, visible
- AI for social protection: Mind the people
- Senator Klobuchar “nudges” social media companies to improve content moderation
- Priorities for advancing women’s equal political leadership in the coming year
| Harnessing the Infrastructure Investment and Jobs Act to train the next generation of workers Posted: 23 Feb 2022 12:45 PM PST By Joseph Kane, Jack Mills The Infrastructure Investment and Jobs Act (IIJA) offers an unprecedented opportunity to accelerate momentum around careers that pay higher wages, require shorter-term credentials, and need a new generation of talent. As millions of workers around the country struggle, leaders need to be ready to harness this funding in ways that expand opportunities to the full diversity of our workforce—women and men, the unemployed and underemployed, and younger students and adult learners. A recent event held by Brookings Metro and the Council for Adult and Experiential Learning (CAEL) explored strategies to do just that. It featured insights from those who will have to do a lot of the heavy lifting: employers, workforce development leaders, economic development officials, and many others at the local, state, and federal level. As event participants discussed, the IIJA is an economic down payment to support lasting growth. Alongside other pending federal legislation, it not only has the potential to accelerate historic investments in transportation, water, energy, broadband, and other systemwide improvements, but it is also shining light on what we must achieve for the infrastructure workforce in the months and years to come. That's particularly true when it comes to climate-focused jobs such as those in renewable energy and electric vehicles. However, simply throwing more money at infrastructure and climate improvements is no guarantee that training investments and high-quality job opportunities will be accessible to all people and all places. Federal, state, and local leaders who collectively own and operate these systems must better coordinate to maximize the reach of this funding. This is an opportunity for short-term growth, and more importantly, for longer-term growth in both our local economies and individual economic mobility. We are in a crucial implementation phase where we can't just pay lip service to infrastructure's economic potential—we need to maximize infrastructure's economic impact. If transportation departments, water utilities, and other IIJA-eligible entities do not find and train enough workers—and soon—we risk squandering this opportunity. To start, we need to better define that opportunity. Too often, policymakers, researchers, and other leaders focus on a bean-counting exercise: trying to calculate the precise number of "shovel-ready" or "green" jobs. They also tend to overemphasize a narrow range of activities and work tasks, usually involved in short-term construction. We need to take a step back and recognize which workers are going to fill a broad range of occupations involved in construction, maintenance, and design. Previous Brookings research has shown that more than three-quarters of these jobs are involved in the long-term operation of a facility or company, from engineers to electricians to technicians, as well as other service positions involved in finance, IT, and human resources. In other words, you don't have to wear a hard hat to pursue an infrastructure career; the job opportunities that are buoyed by infrastructure investments are much broader than many discussions might suggest. We also need to better describe the challenge. Many of these jobs pay more competitive and equitable wages compared to all jobs nationally, and demonstrating the skills they require can often be done in ways that don't neglect those who lack the formal educational barriers to entry. Around half (53.4%) of infrastructure workers have a high school diploma or less, compared to around a third (31.7%) of all workers nationally. Still, other barriers stop many workers from these careers; for example, women currently fill less than 20% of these jobs, and people of color remain underrepresented as well. Overcoming this challenge is crucial since many current workers in the skilled trades and other infrastructure positions are aging and retiring, leading to huge gaps that must be filled. As featured in the Brookings-CAEL event, it will take the combined efforts of multiple regional and national leaders to harness IIJA funding in ways that reach more and different types of workers, specifically by focusing on:
These issues only scratch the surface of what federal, state, and local leaders need to do around infrastructure workforce development amid an influx of new funding. The infrastructure talent pipeline is increasingly empty, and the lack of skilled workers may pose challenges to getting projects done. These workforce gaps could also very likely reduce the impact of IIJA funding and hold back benefits for communities over time. But as conversations evolve among these leaders in the coming months, they have an opportunity to further refine and test potential applications of this generational funding—to not simply reinforce existing and inequitable workforce gaps, but to create stronger pathways for all prospective workers, including those underrepresented in infrastructure and infrastructure-adjacent occupations. Doing so has the potential to expand economic opportunity well beyond the IIJA's initial launch and into years to come. This posting includes an audio/video/photo media file: Download Now |
| How does the Fed define “maximum employment”? Posted: 23 Feb 2022 10:23 AM PST By Lorena Hernandez Barcena, David Wessel Congress instructs the Federal Reserve to aim for maximum employment and price stability. The Fed has defined price stability as inflation averaging 2%, but maximum employment doesn't lend itself to such a simple measure. In its monetary policy strategy statement, the Federal Open Market Committee (FOMC), the Fed's policy-setting body, says: "The maximum level of employment is a broad-based and inclusive goal that is not directly measurable and changes over time owing largely to nonmonetary factors that affect the structure and dynamics of the labor market….[T]he Committee’s policy decisions must be informed by assessments of the shortfalls of employment from its maximum level, recognizing that such assessments are necessarily uncertain and subject to revision. The Committee considers a wide range of indicators in making these assessments." What does that mean in practice? What is maximum employment?Simply put, maximum employment – sometimes called full employment – is the highest level of employment the economy can sustain without generating unwelcome inflation. It describes an economy in which nearly everyone who wants to work has a job. The unemployment rate is one important way to gauge whether an economy is at maximum employment, but not the only one. What does the unemployment rate measure?The headline unemployment rate (U-3) is defined by the Bureau of Labor Statistics (BLS) as the percentage of adults who do not have a job, have actively sought work in the last four weeks, and are currently able to work. The unemployment rate is a percentage of the labor force, the sum of the unemployed plus the employed. This measure doesn't, however, account for all idle workers and isn't a sufficient measure of what's called slack in the labor market. It doesn't, for instance, count workers who have given up looking for work or those who work part-time because they can't find a full-time job. The BLS publishes several alternative measures. The U-6 measure, for instance, counts the unemployed plus discouraged workers (those who'd like to work but have given up looking because they believe there are no jobs available for them), those who are marginally attached to the labor force (those who'd like to work but have stopped looking for work for any other reason), and those working part-time who'd prefer full-time jobs. Why doesn't the Fed just look at the unemployment rate?Primarily because for much of the past decade, the unemployment rate fell and inflation didn't rise. "Although the unemployment rate is a very informative aggregate indicator, it provides only one narrow measure of where the labor market is relative to maximum employment," Fed Governor Lael Brainard has said. "For nearly four decades, monetary policy was guided by a strong presumption that accommodation should be reduced preemptively when the unemployment rate nears its normal rate in anticipation that high inflation would otherwise soon follow. But changes in economic relationships over the past decade have led trend inflation to run persistently somewhat below target and inflation to be relatively insensitive to resource utilization." In a subtle but significant change to its monetary policy strategy statement, the Fed said in August 2020 that it would respond to "shortfalls of employment from its maximum level" rather than the previous "deviations from its maximum level." This indicated that the Fed would no longer preemptively tighten monetary policy only because unemployment was approaching or even falling below estimates of the unemployment rate that economist models suggest are consistent with stable inflation. "This change signals that high employment, in the absence of unwanted increases in inflation or other risks that could impede the attainment of the Committee's goals, will not by itself be a cause for policy concern," the Fed said. What does "broad-based and inclusive" mean?The Fed defines the maximum level of employment as a "broad-based and inclusive goal." When Fed Chair Jerome Powell announced the addition of that phrase to the Fed's strategy statement, he said it "reflects our appreciation for the benefits of a strong job market, particularly for many in low- and moderate-income communities." This reflects calls for the Fed to keep interest rates lower as a way to boost employment in communities, including communities of color, where people are more likely to be unemployed. It also argues for looking beyond the overall unemployment rate to decide whether the economy is at maximum employment. What this means is practice for Fed policy remains to be seen. Some observers have argued that the Fed should keep interest rates low until the Black unemployment rate falls. But Powell has said, "The point of the broad and inclusive goal was not to target a particular unemployment rate for any particular group… And one of the things we look at is unemployment rates and participation rates and wages for different demographic and age groups and that kind of thing." Among the other measures of the labor market that the Fed and others track are the following. What is the Labor Force Participation rate?The Labor Force Participation (LFP) rate is the number of employed people plus the officially unemployed divided by the civilian non-institutionalized population older than 16. In recent years, the LFP rate has been declining as the Baby Boomer generation ages and retires. To look beyond that demographic change, economists often focus on the LFP for people between the ages of 25 and 54, so-called "prime age" because people in this age group are more likely to be available to work. When the prime age LFP rate falls, it means there are more workers on the sidelines of the economy who aren't counted as unemployed but who may be drawn into the labor force. In economic downturns, LFP often declines as people stop looking for work. During the pandemic, the LFP rate fell sharply as many parents (particularly mothers) left the labor force due to childcare facility closures and schools shifting to distance learning, and others dropped out for fear of COVID or other reasons, and still others took early retirement. The Fed and other economists have been surprised that the LFP didn't rebound more quickly when vaccines became available and lockdowns ended. The failure of the LFP rate to return quickly to pre-pandemic levels led the Fed in late 2021 and early 2022 to judge that the economy was closer to maximum employment than it had anticipated. Powell noted that Fed officials hope "the level of maximum employment… consistent with stable prices may increase… as [labor force] participation gradually rises."
What is the employment to population ratio?The employment to population ratio is the employed as a percentage of the civilian noninstitutionalized population. It reflects those people who are counted as unemployed and those who are not working for some other reason—those who are retired as well as those who have given up looking for work. What are quits?Using a sample of 16,000 employers, the Bureau of Labor Statistics' Job Openings and Labor Turnover Survey (JOLTS) measures the number of people who have left their jobs. The quits rate counts workers who voluntarily left their job as a percent of total employment. The layoffs and discharges rate includes all involuntary separations initiated by the employer. Retirements, transfers, deaths, and disability-related separations are counted in the other separations rate. Workers are more likely to quit when they feel confident they can obtain another job, so a rising quit rate is a sign of a very strong job market. What are job openings?JOLTS also counts the number of positions for which employers are actively recruiting and would start within 30 days of hire. The number of unfilled jobs is a measure of the unmet demand for labor. The ratio of the number of unemployed per job opening is a way to gauge the strength of the job market; the lower this ratio, the closer the economy is to maximum employment.
What is the Beveridge Curve?Named for William Beveridge, a 20th-century British labor economist and politician (though he apparently never drew it), the Beveridge Curve charts the number of job postings (as a percentage of all filled and unfilled jobs) against unemployment rate. The Bureau of Labor Statistics updates the chart monthly here. The line generally slopes downward because a higher rate of unemployment usually coincides with a lower rate of vacancies, since there are more people looking for jobs. Outward shifts in the curve (that is, up and to the right) show a given level of job postings is associated with higher rates of unemployment. They are seen as indicators of unwelcome change in the labor market—an increase in mismatches between the skills of workers and the demands of employers, for instance, or a reluctance of jobless workers to take available jobs. The Beveridge Curve did shift outward following the Great Recession. It shifted further outward during and after the COVID-19 pandemic; in other words, employers found it harder to hire at given rates of unemployment than they had in the recent past. When the unemployment rate fell to 4.2% in November 2021, the job openings rate was 6.6%. In September 2017, when the unemployment rate also hit 4.2%, the job openings rate was 4.1%. What is the NAIRU?The NAIRU (Non-Accelerating Inflation Rate of Unemployment) is an estimate of the lowest the unemployment rate can go without leading to rising inflation. The logic is that when there aren't very many unemployed workers, employers raise wages and that leads to rising prices. The NAIRU is difficult to estimate precisely and can change over time as, among other factors, demographics, union strength, and the pace of productivity change. Did the Fed consider the U.S. to be at maximum employment at the beginning of 2022?At his January 2022 press conference, amid growing concern about rising inflation, Powell said that "most FOMC participants agree that labor market conditions are consistent with maximum employment," which he defined as "the highest level of employment that is consistent with price stability." The issue, Powell added, is "whether we can raise [interest] rates and move to a less accommodative [monetary policy]… without hurting the labor market." The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.
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| The Summit for Democracy commitments are out—now what? Posted: 23 Feb 2022 08:35 AM PST By Norman Eisen, Mario Picon, Robin Lewis, Renzo Falla, Lilly Blumenthal On February 14, 2022, two months after the first Summit for Democracy, the U.S. Department of State released written commitments from 56 governments focused on strengthening democracy, combatting corruption, and defending human rights. Now the post-summit Year of Action can begin in earnest. As two of us discussed in a post right after the Summit, for the event to achieve its objectives, civil society, the private sector, and other good governance champions must work with and hold governments accountable for the implementation of concrete, measurable, and meaningful commitments. From our initial survey, we observe significant variation in terms of the specificity and nature of commitments published thus far. Here, we offer a brief snapshot of the distribution of countries with published commitments, the range of those commitments, and their significance.[1] Our initial reactions are preliminary; this post offers a roadmap for the deeper reading and analysis of the commitments that we and many others will undertake. The countries that have submitted written commitments to date fall along the spectrum of governance regimes, as defined by the recently released Democracy Index 2021 from the Economist Intelligence Unit. [2] 53 of 167 countries featured in the index provided written commitments with clear over-representation of those classified as full democracies—18 out of 21 full democracies submitted commitments.[3] Meanwhile, 26 out of 53 countries considered flawed democracies submitted commitments. An even smaller group of hybrid regimes (that is, ones that combine democratic and autocratic features; 8 out of 34 countries) and a minuscule proportion of countries under what are considered authoritarian regimes (1 out of 59 countries) responded to the call for written commitments. Among these submissions, the nature of the commitments varies. Most countries offer some commitments on the domestic front, but many, particularly the full democracies, focus on the international arena. As examples, the Democratic Republic of the Congo's commitments include "organizing elections within constitutional deadlines," while New Zealand's include a pledge of "1 million NZD to support anti-corruption within the Pacific region." Some countries made highly specific commitments, while others shared only broad objectives or a declaration of intentions. While not the sole indicator of a commitment's robustness, specific information such as measurable outputs and the relevant actors, resources, and mechanisms needed for implementation is valuable. This information better enables other participating governments, civil society, and other good governance champions to assess the extent of progress toward stated goals—and help hold governments accountable to meeting them. We can look to the Summit's host, the United States, for an example of commitments that reference specific elements such as relevant actors and resources. For instance, to help advance the new U.S. anti-corruption strategy, the State Department, collaborating with the Departments of Treasury and Justice, will "provide up to $15.1 million to launch the Democracies Against Safe Havens Initiative, which will work to build the capacity of partner governments to deny corrupt actors the ability to hide ill-gotten gains through anti-money laundering measures, to encourage like-minded partners to adopt anti-corruption sanctions and visa restriction regimes, and to detect and disrupt complex corruption schemes." Even for the more robust commitments, multiple factors may limit their impact. We will need to evaluate the commitments in terms of these possible obstacles so they can be addressed and overcome. For example, as our Leveraging Transparency to Reduce Corruption team has noted in our TAP-Plus framework, the implementation of initiatives is conditioned by different dimensions of context.[4] Commitments are subject to the enabling or constraining governance ecosystem in which they are made. For example, promoting a new law to advance good governance does not mean that the law will pass, as political dynamics or other factors may impede it, or that such a law will actually achieve its intended goal in terms of anti-corruption, expansion of rights, or human rights protection (as other legislation may limit its impact or weaken its enforcement). To put an even sharper point on it, the credibility and effectiveness of commitments may be eroded by disconnects between them and the existing governance conditions. Without meaningful reform and engagement with civil society, commitments—particularly those made in challenging governance contexts such as environments with limited media freedom, constrained civic space, and low political trust—will remain, at best, aspirational. We discuss these and other contextual factors that enable or constrain the success of efforts to advance democracy and reduce corruption in our TAP-Plus report. We will use that framework, among others, as we consider the deeper analysis that these commitments merit. While the commitments (like the process of developing them) are sure to feature imperfections, they represent a starting point for debate and further development. They must be studied, gaps and opportunities identified, and implementation and supplementation undertaken. Independent analysis is critical for assessing whether commitments address key governance challenges and support the objectives of advancing democracy, defending human rights, and combating corruption. Promoting inclusive dialogue and debate within and between countries, civil society, and other stakeholders will be essential to fulfill the objectives of the Summit. The Open Government Partnership (OGP) is one such platform for engagement. Mechanisms like OGP—with domestic action plans, accountability mechanisms, and space for dialogue—will be important for ensuring follow-through, as will efforts of all concerned at the local, national, and global levels to advance accountability. Nor should we look only to 2022 and the follow-up Summit planned for its conclusion. For meaningful progress to be made, we must consider the longer time horizon in which commitments unfold. The Year of Action must be turned into years of action. To support these efforts, we look forward to actively participating with others in the work of discussing, monitoring, and analyzing the commitments and their implementation with a particular focus on anti-corruption efforts at the country and international levels. We are at an inflection point in combating corruption and advancing democracy, and together we can seize it. The stakes could not be higher. The authors thank Matthew Eitel for fact-checking and copyediting assistance. Footnotes [1] As noted on the Summit for Democracy's written commitments page, the list of 56 countries "reflects those that submitted commitments in writing; if a hyperlink is missing that means the commitments are still undergoing a mandatory accessibility compliance review. […] Additional commitments will be processed as received from delegations and posted online.” We have described our initial impressions of the written commitments available as of February 14, 2022. [2] For the full descriptions of regime types, see page 68 of the Democracy Index 2021 report. [3] Note that three countries that have submitted written commitments (Samoa, Maldives, and Kosovo) are not included under the Democracy Index ratings. [4] TAP stands for transparency, accountability, and participation, the traditional trifecta of open governance initiatives. This posting includes an audio/video/photo media file: Download Now |
| Educating children to make the invisible, visible Posted: 23 Feb 2022 07:45 AM PST By Ryan F. Lei, Sa-Kiera T.J. Hudson, Kathy Hirsh-Pasek Just days after Associate Justice Stephen Breyer announced that he would retire from the Supreme Court, President Joe Biden said that his nominee for the position would be a Black woman. Biden's remarks drew immediate backlash. Explicitly identifying race and gender as important factors in critical decisions—the argument went—adds bias to what otherwise should be an unbiased neutral decision process. Decades of social science research, however, document the many ways in which race and gender do matter—from biased hiring decisions, to wealth inequality, and even our most intimate romantic relationships—whether we pay attention to their impact or not. Bias is learned. And bias manifests itself in the assumptions we make about how the social world functions. Even as children, we begin to develop representations that center white people as who we see as "default" when we think of men, boys, and girls—that is, who automatically and naturally comes to mind when we think of a category or group. These defaults set the foundation for systematic inequality in all sectors of society. For example, in many leadership positions, we tend to think of white men as the normative choice, reflecting assumptions of white people and men as cultural defaults. In fact, even as the United States has become more diverse, over 80 percent of positions of power are held by white people.
The Supreme Court is no exception. In the history of the Supreme Court, 95 percent of justices have been white men. Racial minorities and women have been excluded from consideration for this highest court in America for centuries, with the first Black justice (Thurgood Marshall) being appointed only as recently as 1967 and the first female justice (Sandra Day O'Connor) being appointed even more recently in 1981. Thus, gender and race have always mattered in selection of justices, but not explicitly named until now. Of course, bringing light to the invisibility of these dynamics often leads to ostensible concerns about "quality." That is, there is an assumption that a concerted focus on race and gender means sacrificing the quality of the candidate. Not only is such a refrain wrong, it fails to account for the fact that the credentialing that often goes into considerations of "quality" are themselves biased, given that racial minorities and women have historically been excluded from many elite institutions of higher education. Even as the United States has made progress in diversifying positions of power, gendered-racial dynamics continue to perpetuate the exclusion of Black women, who are societally considered to be nonrepresentative of either the gender or racial groups. And, patterns of excluding Black women from being representative of their gender and racial groups emerges early. Recent research suggests that by age five and a half, children are slower to recognize Black women as part of their gender group and are less likely to attribute feminine characteristics to Black women. These beliefs reflect a tendency to attribute traits to the person, instead of the way that society is structured. However, educating children to think about what the structural causes of inequality are can help prompt them to recognize the unfairness being perpetuated. Although these conversations can be uncomfortable, talking with children is critical to helping them recognize the structural inequalities that exist, instead of leaving them to develop explanations that attribute disparate outcomes to the individual. And, for girls and children from racial minority backgrounds, seeing themselves represented in positions of power can be motivating. Given the psychological invisibility of Black women in American society—including in how Black women are marginalized when children learn about gender categories—the question is not whether Joe Biden should consider a Black woman for the Supreme Court, but rather, how could he not? This posting includes an audio/video/photo media file: Download Now |
| AI for social protection: Mind the people Posted: 23 Feb 2022 07:35 AM PST By Michael Lokshin, Nithin Umapathi The technology that allowed passengers to ride elevators without an operator was tested and ready for deployment in the 1890s. But it was only after the elevator operators' strike of 1946—which cost New York City $100 million—that automated elevators started to get installed. It took more than 50 years to persuade people that they were as safe and as convenient as those operated by humans. The promise of radical changes from new technologies has often overshadowed the human factor that, in the end, determines if and when these technologies will be used. Interest in artificial intelligence (AI) as an instrument for improving efficiency in the public sector is at an all-time high. This interest is motivated by the ambition to develop neutral, scientific, and objective techniques of government decisionmaking (Harcourt 2018). As of April 2021, governments of 19 European countries had launched national AI strategies. The role of AI in achieving the Sustainable Development Goals recently drew the attention of the international development community (Medaglia et al. 2021). Advocates argue that AI could radically improve the efficiency and quality of public service delivery in education, health care, social protection, and other sectors (Bullock 2019; Samoili and others 2020; de Sousa 2019; World Bank 2020). In social protection, AI could be used to assess eligibility and needs, make enrollment decisions, provide benefits, and monitor and manage benefit delivery (ADB 2020). Given these benefits and the fact that AI technology is readily available and relatively inexpensive, why has AI not been widely used in social protection? Limited deploymentAt-scale applications of AI in social protection have been limited. A study by Engstrom and others (2020) of 157 public sector uses of AI by 64 U.S. government agencies found seven cases related to social protection, where AI was mainly used to predict risk screening of referrals at child protection agencies (Chouldechova and others 2018; Clayton and others 2019). Only a handful of evaluations of AI in social protection have been conducted, including assessments of homeless assistance (Toros and Flaming 2018), unemployment benefits (Niklas and others 2015), and child protection services (Hurley 2018; Brown and others 2019; Vogl 2020). Most of them were based on proofs-of-concept or pilots (ADB 2020). Examples of successful pilots include automation of Sweden's social services (Ranerup and Henriskon 2020) and experimentation by the government of Togo with machine learning using mobile phone metadata and satellite images to identify households most in need of social assistance (Aiken and others 2021). Some debacles have reduced public confidence. In 2016, Services Australia—an agency of the Australian government that provides social, health, and child support services and payments—launched Robodebt, an AI-based system designed to calculate overpayments and issue debt notices to welfare recipients by matching data from the social security payment systems and income data from the Australian Taxation Office. The new system erroneously sent more than 500,000 people debt notices to the tune of $900 million (Carney 2021). The failure of the Robodebt program has had ripple effects on public perceptions about the use of AI in social security administration. In the United States, the Illinois Department of Children and Family Services stopped using predictive analytics in 2017, based on warnings by staff that the poor quality of the data and concerns about the procurement process made the system unreliable. The Los Angeles Office of Child Protection terminated its AI-based project, citing the "black-box" nature of the algorithm and the high incidence of errors. Similar problems of data quality marred the application of a data-driven approach to identifying vulnerable children in Denmark (Jørgensen 2021), where a project was halted in less than a year, even before it was fully implemented. The human factor in the adoption of AI for social protectionResearch on the use of AI in social protection draws at least four cautionary tales of the risks involved and the consequences for people's lives of algorithmic biases and errors. The accountability and "explainability" problem: Public officials are often required to explain their decisions—such as why someone was denied benefits—to citizens (Gilman 2020). However, many AI-based outcomes are opaque and not fully explainable because they incorporate many factors in multistage algorithmic processes (Selbst et al. 2018). A key consideration for promoting AI in social protection is how AI discretion fits within the welfare system's regulatory, transparency, grievance addressal, and accountability frameworks (Engstrom 2020). The wider risk is that without adequate grievance redressal systems, automation may disempower citizens, especially minorities and the disadvantaged, by treating citizens as analytical data points. Data quality: The quality of administrative data profoundly affects the efficacy of AI. In Canada, the poor quality of the data created errors that led to subpar foster placement and failure to remove children from unsafe environments (Vogl 2020). The tendency to favor legacy systems can undermine efforts to improve the data architecture (Mehr and others 2017). Misuse of integrated data: The applications of AI in social protection require a high degree of data integration, which relies on data sharing across agencies and databases. In some instances, data utilization could morph into data exploitation. For example, the Florida Department of Child and Family collected multidimensional data on students' education, health, and home environment. However, this data has since been interfaced with the Sheriff's Office's records to identify and maintain a database of juveniles who are at risk of becoming prolific offenders. In such cases, data integration creates new opportunities for controversial overreach, deviating from the intentions under which data was originally collected (Levy 2021). Response of public officials: The adoption of AI should not presume that welfare officials can easily transform themselves from claims processors and decisionmakers to managers of AI systems (Renerup and Henrisksen (2020) and Brown et al. (2019). The way public officials respond to the introduction of AI-based systems may influence such system performance and lead to unforeseen consequences. In the U.S., police officers have been found to disregard recommendations of the predictive algorithms or use this information in ways that can impair system performance and violate assumptions about its accuracy (Garvie 2019). Public response and public trust: Using AI to make decisions and judgments about the provision of social benefits could exacerbate inclusion and exclusion errors because of data-driven biases and ethical concerns around accountability for life-altering decisions (Ohlenburg 2020). Thus, building trust in AI is vital to scaling up its use in social protection. However, a survey of Americans shows that almost 80 percent of respondents have no confidence in the ability of governmental organizations to manage the development and use of AI technologies (Zhang and Dafoe 2019). These concerns fuel growing efforts to counteract AI-based systems' potential threats to people and communities. For example, AI-based risk assessments are challenged on due-process-related grounds, as in denying housing and public benefits in New York (Richardson 2019). Mikhaylov, Esteve, and Campion (2018) argue that for governments to use AI in their public services, they need to promote its public acceptance. Future of AI in social protectionToo few studies have been conducted to suggest a clear path for scaling the use of AI in social protection. But it is clear that the system design must consider the human factor. Successful use of AI in social protection requires an explicit institutional redesign, not mere tool-like adoption of AI in a pure information technology sense. Using AI effectively requires coordination and evolution of the system's legal, governance, ethical, and accountability components. Fully autonomous AI discretion may not be appropriate; a hybrid system in which AI is used in conjunction with traditional systems may be better to reduce risks and spur adoption (Chouldechova and others 2018; Ranerup and Henrikson 2020; Wenger and Wilkins 2009; Sansone 2021). The international development institutions could help countries address the people-centric challenges within the public sector as part of new technology adoption. That is their comparative advantage over the tech sector. Investments in research on the bottlenecks in utilizing AI for social protection could yield high development returns. This posting includes an audio/video/photo media file: Download Now |
| Senator Klobuchar “nudges” social media companies to improve content moderation Posted: 23 Feb 2022 07:03 AM PST By Mark MacCarthy Senator Amy Klobuchar's new bipartisan social media bill, which she introduced on February 9 in conjunction with Republican Senator Cynthia Lummis, is actually two bills in one. It is a thoughtful and promising attempt to craft content-neutral ways of reducing "social media addiction and the spread of harmful content." It is also by far the most ambitious attempt in the United States to require detailed transparency reports from the larger social media companies. As such, it deserves the careful consideration of lawmakers on both sides of the aisle, including a review of important First Amendment issues, followed by prompt Congressional action. Nudges and interventionsS. 3608, the ''Nudging Users to Drive Good Experiences on Social Media Act'' or the ''Social Media NUDGE Act,” requires the National Science Foundation and the National Academies of Sciences, Engineering, and Medicine to conduct an initial study, and biennial ongoing studies, to identify "content-agnostic interventions" that the larger social media companies could implement "to reduce the harms of algorithmic amplification and social media addiction." After receiving their report on the initial study, due a year after the enactment of the law, the Federal Trade Commission would be required to begin a rulemaking proceeding to determine which of the recommended social media interventions should be made mandatory. What interventions are the bill's authors thinking of? The bill lists examples of possible content-neutral interventions that "do not rely on the substance" of the material posted, including "screen time alerts and grayscale phone settings," requirements for users to "read or review" social media content before sharing it, and prompts (that are not further defined in the bill) to "help users identify manipulative and microtargeted advertisements." The bill also refers approvingly to "reasonable limits on account creation and content sharing" that seem to concern circuit breaker techniques to limit content amplification. In addition, the bill goes into great detail in mandating that social media companies publish public transparency reports every six months, and with a distinct focus on correcting some of the weaknesses of the current transparency reports that critics have noted. For instance, it requires the larger social media companies to calculate "the total number of views for each piece of publicly visible content posted during the month and sample randomly from the content." It would also require information about content posted and viewed that was reported by users, flagged by an automated system, removed, or restored or labeled, edited otherwise moderated. This focus on the details of reports is a welcome addition to other approaches that remain at a higher level of generality. Critics blame algorithms for many of the ills on social media, and policymakers around the world are seeking to hold social media companies responsible for the online harms they algorithmically amplify. But no one at this point really knows how social media algorithms affect mental health, nor political beliefs and actions. More importantly, no one really knows what changes to algorithms would improve things. Skeptics of an algorithmic fix to the ills of social media focus on the difficulty of disentangling cause and effect the social media world. "Is social media creating new types of people?" asked BuzzFeed's senior technology reporter Joseph Bernstein in a 2021 Harper's article, "or simply revealing long-obscured types of people to a segment of the public unaccustomed to seeing them?" Other skeptics point to a genuine weakness in an algorithmic fix to the problems of disinformation, misinformation, and hate speech online. "It's a technocratic solution to a problem that's as much about politics as technology," says New York Times columnist Ben Smith. He adds, "the new social media-fueled right-wing populists lie a lot, and stretch the truth more. But as American reporters quizzing Donald Trump's fans on camera discovered, his audience was often in on the joke." Even though cause and effect are hard to discern in social media, it is undeniable that algorithms contribute to hate speech and other information disorder on social media. The problem is not that algorithms have no effect and we are imagining a problem that doesn't exist. Nor is the problem that nothing works to counteract the effect of misinformation and hate speech online, or that we know nothing about effective interventions. The problem is that we do not know enough to mandate algorithmic solutions or require specific technical or operational interventions, especially those that overly surveil certain populations. Until a lot more is known about the extent and causes of the online problems and the effectiveness of remedies, legislators should not be seeking to mandate specific techniques in legislation. The matter is one for experimentation and evidence, not one for intuitions about what is most likely to work. The NUDGE bill takes this evidence-based approach. It requires the government's science agencies that rely on the academic community for expertise to take the lead in generating the recommendations for algorithmic interventions. To prevent the agency from improvising on its own, it explicitly prevents the agency from mandating any intervention that has not been addressed in the reports from the national academies. Some needed improvementsSeveral improvements in the bill seem important to me. The first is to give the researchers working with the national science agencies full access to all the information they need to conduct their studies. The bill improves on existing public transparency reports but it does not provide for needed access to internal social media data for vetted researchers. What the bill's mandated transparency reports make available to the public might not be enough for the researchers to determine which interventions are effective. They should be allowed broad, mandated access to internal social media data including internal studies and confidential data about the operation of content moderation and recommendation algorithms. Only with this information will they be able to determine empirically which interventions are likely to be effective. The bill is prudent to require the science agencies to conduct ongoing studies of interventions. A second improvement in the bill would be to require the FTC to update its mandated interventions in light of these ongoing studies. The first set of mandated interventions will be almost certainly only moderately effective at best. Much will be learned from follow-on assessments after the first round of interventions have been put into practice. The FTC should have an obligation to update the rules in light of the new evidence it receives from the science agencies. The cloud on the horizonAs promising as it is, there is a cloud on the horizon that threatens the entire enterprise. The bill's objective of reducing harmful content is in tension with its mechanism of content-neutral interventions. How can the science agencies and the regulatory agency determine which interventions are effective in reducing harmful content without making content judgments? As Daphne Keller has noted, it is actually not all that hard to slow down the operation of social media systems through the insertion of circuit-breakers such as limits on the "number of times an item is displayed to users, or an hourly rate of increase in viewership." Such rules would restrict all speech exceeding these limits: both important breaking news such as the videos documenting the death of George Floyd, as well as the latest piece of viral COVID misinformation. But the more fundamental concern is that policymakers do not want rules that are neutral in their effect. They want interventions that allow the rapid distribution of real breaking news and new insightful commentaries on issues of public importance while impeding hate speech, terrorist material, and content that is harmful to children's health. They want, in other words, technical proxies for harmful speech, not interventions that slow everything down. Keller rightly worries whether neutral circuit breaker rules "would have neutral impact on user speech" because she feels that the First Amendment might frown on rules that have a disproportionate effect on certain content, even if the rules do not assess the content itself. For this reason, it is important for the policy community to engage in a thoughtful assessment of the First Amendment implications of the NUDGE bill. My own instinct is that just as the Courts have permitted race-and-gender neutral proxies to achieve disproportionate gains for minorities and women in affirmative action cases, the Courts will allow a similar reliance on content-neutral proxies to filter out harmful online content. But proponents of this bill need to consider how to position it for an inevitable First Amendment challenge even as they begin the process of moving it through the legislative process. This posting includes an audio/video/photo media file: Download Now |
| Priorities for advancing women’s equal political leadership in the coming year Posted: 23 Feb 2022 07:00 AM PST By Chiedo Nwankwor
Arguments for women's equal leadership participation in politics have highlighted its intrinsic value and instrumental justifications. Its fundamental goal—articulated within claims of democratic justice, equity and human rights—is expressed in the multiple international agreements, regional frameworks, and national laws driven by women's movements and feminists' mobilizations. The instrumental rationale centers on arguments of the policy-responsiveness of political representatives towards those they represent. Specifically, it supports the expectations that women's presence in political leadership will lead to inclusive decisions that reflect the needs and interests of a broader population, including women and girls. In effect, women's political leadership results in optimal governance outcomes for most of society.
While there is no shortage of intelligent, ambitious, and capable African women potential leaders, multiple obstacles hinder women's leadership aspirations and candidacy. These barriers are determined by contextual factors that combine formal and informal rules, institutions and other structural elements in unique ways to limit women's access to power as they shape opportunities and incentives for actors and actions. Although unwritten, informal rules string together expectations from culture, religion, and social structures to exert powerful constraints on women's political agency alongside formal institutions. These structural and cultural barriers, including those of tradition, impose significant limitations on women's access to resources, and place high demands on their time as gender roles increase their responsibility of care in the home, thus creating vast resource and time deficits that benefit men and curtail opportunities of entry and electoral victory for women. Women's equal participation is also impeded by conscious and unconscious biases, discriminatory attitudes and norms, and mobility limitations due to threats of (political) violence that continue to pose obstacles to meaningful leadership participation of women within state institutions. Records of previous and ongoing interventions provide evidence of numerous effective strategies for advancing women's political participation across the region. Thus, I suggest four key policy areas decisionmakers might prioritize for promoting gender equality in political leadership, particularly in the coming year. The starting point must be the transformation of formal, political institutions through constitutional amendments, legal reforms, and targeted affirmative action programs, including but not limited to legislated sex quotas, reservations, and party mandates when combined with adequate safeguards. Political parties must be key targets for transformation as they are central to women's political access and competitiveness. Their provision, or not, of a fair playing ground directly impacts women's access to appointed and elected positions in government. Second, capacity-building interventions for women political leaders within established women parliamentary caucuses provide women leaders with skills and knowledge for effectiveness and success. Meaningful (substantive) representation from women leaders, or the perception thereof, reinforces role-modeling effects and meets voters' demand for performance accountability, which increases public's positive attitude and support for women's political leadership. Training should focus on key actors—gatekeepers and influencers, leadership models and networks, and understanding strategic influence and effective forms of social action for change. Third, interventions towards promoting social mobilization and collective action contributes to changes in the nature of the state and expands space for women's inclusion. While these changes can be progressive, they could also advance a conservative agenda like religious and ethnicity-based civil society mobilizations. Policy entrepreneurs and implementers should be vigilant, therefore, in ensuring that, when mobilized, these coalitions and movements are not hijacked and used to promote myriad agendas that mask closet anti-gender propositions, including traditional notions of women's subjecthood that limits their opportunities for political leadership.
Finally, promoting a conducive and gender-inclusive society constitutes an important variable in the tension between positive changes in women's leadership participation and persistent sexist attitudes. Gender norms and behaviors have shaped women's experience in running for and holding political offices. For those women who are elected, norms also shape their leadership experience, including how they are perceived and treated by the public and by fellow political actors. Inequalities emanating from social discrimination are reflected in informal rules, which require more deep-seated structural reconfiguration. Interventions that aim to correct these norms can become catalysts for change. In the long term, exposure to female leadership can also alter social norms. While norms and social attitudes change in modest increments over long periods, interventions targeting traditional and regressive gender norms have been known to promote conducive and gender-inclusive societies. The relationship between political change and social change is complex. Political change is nothing if there is no social change in the way men perceive women, and political change might be inaccessible without social change. As such, ensuring social change can guarantee political change, including equal women's political representation across formal bodies of the state. As we contemplate the new year, we don't start from nothing; we have lessons on which to build. In fact, the COVID-19 pandemic highlighted two simultaneous and contrasting patterns in its impact on gender equality in general and specifically, women's political leadership. On the one hand, it spotlighted the fragility of progress on gender equality, evidenced in the stunning reversal of gains made over decades in just two years. On the other hand, it highlighted that inclusive politics provides a template for effective governance while women's equal political participation offers pathways for policy change by establishing stronger social nets and systems for more resilient societies. The future is equal. This posting includes an audio/video/photo media file: Download Now |
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