Alex John London

Alex John London, Hoda Heidari

The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum's capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders' ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception.

Divyansh Kaushik, Zachary C. Lipton, Alex John London

In recent years, machine learning (ML) has relied heavily on crowdworkers both for building datasets and for addressing research questions requiring human interaction or judgment. The diverse tasks performed and uses of the data produced render it difficult to determine when crowdworkers are best thought of as workers (versus human subjects). These difficulties are compounded by conflicting policies, with some institutions and researchers regarding all ML crowdworkers as human subjects and others holding that they rarely constitute human subjects. Notably few ML papers involving crowdwork mention IRB oversight, raising the prospect of non-compliance with ethical and regulatory requirements. We investigate the appropriate designation of ML crowdsourcing studies, focusing our inquiry on natural language processing to expose unique challenges for research oversight. Crucially, under the U.S. Common Rule, these judgments hinge on determinations of aboutness, concerning both whom (or what) the collected data is about and whom (or what) the analysis is about. We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets. Our analysis exposes a potential loophole in the Common Rule, where researchers can elude research ethics oversight by splitting data collection and analysis into distinct studies. Finally, we offer several policy recommendations to address these concerns.

Alex John London, Aydin Mohseni

Standard decision frameworks address uncertainty about facts but assume fixed options and values. We extend the Jeffrey-Bolker framework to model refinements in values and prove a value-of-information theorem for axiological refinement. In multi-agent settings, we establish that mutual refinement will characteristically transform zero-sum games into positive-sum interactions and yield Pareto-improvements in Nash bargaining. These results show that a framework of rational choice can be extended to model value refinement. By unifying epistemic and axiological refinement under a single formalism, we broaden the conceptual foundations of rational choice and illuminate the normative status of ethical deliberation.

Zeyu Tang, Alex John London, Atoosa Kasirzadeh et al. · stanford

Social determinants are variables that, while not directly pertaining to any specific individual, capture key aspects of contexts and environments that have direct causal influences on certain attributes of an individual. Previous algorithmic fairness literature has primarily focused on sensitive attributes, often overlooking the role of social determinants. Our paper addresses this gap by introducing formal and quantitative rigor into a space that has been shaped largely by qualitative proposals regarding the use of social determinants. To demonstrate theoretical perspectives and practical applicability, we examine a concrete setting of college admissions, using region as a proxy for social determinants. Our approach leverages a region-based analysis with Gamma distribution parameterization to model how social determinants impact individual outcomes. Despite its simplicity, our method quantitatively recovers findings that resonate with nuanced insights in previous qualitative debates, that are often missed by existing algorithmic fairness approaches. Our findings suggest that mitigation strategies centering solely around sensitive attributes may introduce new structural injustice when addressing existing discrimination. Considering both sensitive attributes and social determinants facilitates a more comprehensive explication of benefits and burdens experienced by individuals from diverse demographic backgrounds as well as contextual environments, which is essential for understanding and achieving fairness effectively and transparently.