The U.S. criminal legal system increasingly relies on software output to convict and incarcerate people. In a large number of cases each year, the government makes these consequential decisions based on evidence from statistical software -- such as probabilistic genotyping, environmental audio detection, and toolmark analysis tools -- that defense counsel cannot fully cross-examine or scrutinize. This undermines the commitments of the adversarial criminal legal system, which relies on the defense's ability to probe and test the prosecution's case to safeguard individual rights. Responding to this need to adversarially scrutinize output from such software, we propose robust adversarial testing as an audit framework to examine the validity of evidentiary statistical software. We define and operationalize this notion of robust adversarial testing for defense use by drawing on a large body of recent work in robust machine learning and algorithmic fairness. We demonstrate how this framework both standardizes the process for scrutinizing such tools and empowers defense lawyers to examine their validity for instances most relevant to the case at hand. We further discuss existing structural and institutional challenges within the U.S. criminal legal system that may create barriers for implementing this and other such audit frameworks and close with a discussion on policy changes that could help address these concerns.

It's no secret that artificial intelligence, algorithms, and big data have a problem with gender and racial bias. These systems can be biased based on who builds them, how they're developed, and how they're ultimately used. Trying to solve the problem is a community of Black data scientists, researchers, and organizations. This article highlights the Black women amongst their ranks, who are exposing algorithmic biases, empowering communities of color with data, and arguing for more diverse representation. Joy Buolamwini is a Ghanaian-American computer scientist based at MIT Media Lab.

Rediet Abebe uses algorithms and AI to improve access to opportunity for historically marginalized communities. When Abebe moved from her native Ethiopia to the United States to attend Harvard College, she was struck by how vital resources often fail to reach the most vulnerable people, even in the world's wealthiest nation. She now uses computational techniques to mitigate socioeconomic inequalities. While she was an intern at Microsoft, Abebe formulated an AI project that analyzes search queries to shed light on the unmet health information needs of people in Africa. Her study revealed such information as which demographic groups are likely to show interest in natural cures for HIV and which countries' residents are especially concerned about HIV/AIDS stigma and discrimination.

The artificial intelligence industry is often criticized for failing to think through the social repercussions of its technology--think instituting gender and racial bias in everything facial-recognition software to hiring algorithms. On Monday (March 18), Stanford University launched a new institute meant to show its commitment to addressing concerns over the industry's lack of diversity and intersectional thinking. The Institute for Human-Centered Artificial Intelligence (HAI), which plans to raise $1 billion from donors to fund its initiatives, aims to give voice to professionals from fields ranging from the humanities and the arts to education, business, engineering, and medicine, allowing them to weigh in on the future of AI. "Now is our opportunity to shape that future by putting humanists and social scientists alongside people who are developing artificial intelligence," Stanford president Marc Tessier-Lavigne declared in a press release. But in trying to address AI's blind spots, the institute has been accused of replicating its biases. Of the 121 faculty members initially announced as part of the institute, more than 100 appeared to be white, and a majority were male.

Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an out-sized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of interdisciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities. To realize the opportunities in these domains, we need to correctly set objectives and reason about human behavior and actions. Doing so requires a deep grounding in the field of interest and collaboration with domain experts who understand the societal implications and feasibility of proposed solutions. These insights can play an instrumental role in proposing algorithmically-informed policies. In this article, we describe the Mechanism Design for Social Good (MD4SG) research agenda, which involves using insights from algorithms, optimization, and mechanism design to improve access to opportunity. The MD4SG research community takes an interdisciplinary, multi-stakeholder approach to improve societal welfare. We discuss three exciting research avenues within MD4SG related to improving access to opportunity in the developing world, labor markets and discrimination, and housing. For each of these, we showcase ongoing work, underline new directions, and discuss potential for implementing existing work in practice.