American Civil Liberties Union

In this work, we "audit the audits," analyzing the documents produced pursuant to one of the United States' first enacted laws regulating the use of artificial intelligence in employment: New York City's Local Law 144.This law requires employers and employment agencies using certain types of automated tools to publish "bias audits" with statistics about how different sex and racial groups fare in the hiring process when the tools are used.We collect and conduct a comprehensive analysis of all Local Law 144 bias audits (N=116) made publicly available to our knowledge from the law taking effect in July 2023 until early November 2024, and describe the extensive challenges we faced in identifying, archiving, extracting information from, and ultimately analyzing these bias audits.We identify several ways that bias audits produced in accordance with Local Law 144 are incomplete evaluations of algorithmic bias, despite news coverage and characterizations by employers and vendors suggesting otherwise.We show that Local Law 144 bias audits are significantly hampered by several issues, including missing demographic data, opaque data aggregation, problematic uses of "test data, " and reliance on metrics that do not represent how automated hiring tools are used in practice.We analyze the reported results in Local Law 144 bias audits alongside the four-fifths rule often used as a measure for assessing adverse impact in employment contexts.Most audits do not report results that would not suggest violations of the four-fifths rule.Crucially, however, we show that these tools could often be in violation of the four-fifths rule when considering potential impacts of missing demographic data.We offer ten practical recommendations to strengthen future legislative efforts that mandate algorithm auditing in hiring and other areas, and contribute an open dataset and codebase for extracting and combining bias audit results to support future auditing efforts.

Participatory approaches to implementation science (IS) offer an inclusive, collaborative, and iterative perspective on implementing and sustaining evidence-based interventions (EBIs) to advance health equity. This review provides guidance on the principles and practice of participatory IS, which enables academic researchers, community members, implementers, and other actors to collaboratively integrate practice-, community-, and research-based evidence into public health and health care services. With a foundational focus on supporting academics in coproducing knowledge and action, participatory IS seeks to improve health, reduce inequity, and create transformational change. The three main sections of this review provide (a) a rationale for participatory approaches to research in implementation science, (b) a framework for integrating participatory approaches in research utilizing IS theory and methods, and (c) critical considerations for optimizing the practice and impact of participatory IS. Ultimately, participatory approaches can move IS activities beyond efforts to make EBIs work within harmful systems toward transformative solutions that reshape these systems to center equity.

When disclosing records affirming that they recognize of the month of May as "Mental Health Awareness Month;" the San Francisco Arts Commission (SFAC) denied having held conversations about [1] Amber Heard as a Caucasian woman, who had turned 30 (thirty) years of age on April 22nd 2016; [2] Amber Heard as a woman, who had attended the 2016 Coachella Valley Music and Arts Festival on her 30th birthday; [3] the April 27th 2022 highly publicized accusations made against Amber Heard alleging that she had defecated in the matrimonial bed she shared with her ex-husband on the occasion of her 30th birthday; [4] the racist mental diagnosis known as “drapetomania,” which has now been totally discredited; [5] the mental illness of “schizophrenia” having historically been associated with white women prior to 1960; [6] the reasons why Black/African American people have increasingly been diagnosed with “schizophrenia” after 1960; [7] the reasons why Black/African American people are five to seven times more likely to be diagnosed with “schizophrenia” compared to white people.As a matter of principle, the AACL unequivocally denounces violence committed against women irrespective of their racial backgrounds, their sexual orientations, their national origins, their religious affiliations and/or their disability status. In the judgment of the AACL, Amber Heard was subject of intense online vitriol hatred, which ultimately impacted the outcome of her defamation trial in a very adverse manner (this Calendar Year 2022). The AACL deeply regrets the decision of the judicial branch of the U.S government (the courts) [1] to issue rulings that were not favorable to Amber Heard and [2] to reject the arguments presented in her appeal (which were both convincing and persuasive). Be well. Take care. Keep yourselves at arms distance.Michael Ayele (a.k.a) WAnti-Racist Human Rights ActivistAudio-Visual Media AnalystAnti-Propaganda Journalist

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Abstract For the past 50 plus years the United States has been debating the very nature of our criminal justice system. Are we too lenient? Are we too vindictive? Do we give too much power to our cops and prosecutors or too many protections to defendants? But maybe most important, is there a better way? Can we ensure we have both safety and dignity built into our system? These questions are extremely difficult for law makers to answer because of the moral implications involved with crime and punishment, but also because of one glaring weakness: data sharing and reporting. We simply do not have comprehensive data collection systems for policy makers to draw on to design interventions that can protect public safety and help heal community wounds. While there is no silver bullet that will fix these problems, we believe attention should be paid to: 1) fixing inconsistencies in key terms so data collection can be universal, 2) fixing the issues with delayed reporting so the data we have is up to date for researchers and policy makers, and 3) addressing issues with inadequate and inconsistent data storage so not only will the data be available but assessable to those who can use it to improve the system.