Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency. Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.

Let's play a little game. Imagine that you're a computer scientist. Your company wants you to design a search engine that will show users a bunch of pictures corresponding to their keywords -- something akin to Google Images. You're a great computer scientist, and this is basic stuff! But say you live in a world where 90 percent of CEOs are male. Should you design your search engine so that it accurately mirrors that reality, yielding images of man after man after man when a user types in "CEO"? Or, since that risks reinforcing gender stereotypes that help keep women out of the C-suite, should you create a search engine that deliberately shows a more balanced mix, even if it's not a mix that reflects reality as it is today?

Cities are looking at a number of solutions to these problems. Some require disclosure when an AI model is used in decisions, while others mandate audits of algorithms, track where AI causes harm or seek public input before putting new AI systems in place. What would you like to see cities do to make their use of AI more transparent and fair? It will take time for cities and local bureaucracies to build expertise in these areas and figure out how to craft the best regulations, says Joanna Bryson, a professor of ethics and technology at the Hertie School in Berlin. But such efforts could provide a model for other cities, and even nations that are trying to craft standards of their own, she says.

Job candidates rarely know when hidden artificial intelligence tools are rejecting their resumes or analyzing their video interviews. But New York City residents could soon get more say over the computers making behind-the-scenes decisions about their careers. A bill passed by the city council in early November would ban employers from using automated hiring tools unless a yearly bias audit can show they won't discriminate based on an applicant's race or gender. It would also force makers of those AI tools to disclose more about their opaque workings and give candidates the option of choosing an alternative process -- such as a human -- to review their application. Proponents liken it to another pioneering New York City rule that became a national standard-bearer earlier this century -- one that required chain restaurants to slap a calorie count on their menu items.

MetroLab Network has partnered with Government Technology to bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data and innovation projects underway between cities and universities. If you'd like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information. In this month's installment of the Innovation of the Month series, we explore the work of Julia Stoyanovich, an assistant professor of Computer Science, Engineering, and Data Science at New York University, and Falaah Arif Khan from Data, Responsibly, who are creating comics designed to increase awareness of responsible data science. MetroLab's Ben Levine spoke with the two about the background and development of their project. Ben Levine: Can you tell us about the origin of the Data, Responsibly project and who has been involved in it?

Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the overall representativeness of the selected set. An unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best ones, and this unfairness may not be well-balanced across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the \in-group fairness across groups, and formalize the induced optimization problems as integer linear programs. Using these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints.

Earlier this month researchers from the Massachusetts Institute of Technology and Stanford University reported that they had found that three commercial facial-analysis programs from major tech companies showed bias in both skin-type and gender. The error rates for determining the gender of light-skinned men were 0.8% compared with much higher error rates for darker-skinned women, which in some cases was as much as 20% and 34%. This is not the first time an algorithm powering an AI application has delivered an erroneous -- to say nothing of embarrassing -- result. In 2015, Flickr, a photo-sharing site owned by Yahoo launched image-recognition software that automatically created tags for photos. Some of the tags being created were highly offensive -- such as "sport" and "jungle gym" for pictures of concentration camps and "ape" for pictures of humans including an African American man.