Below see some of my current scholarly interests and projects. Versions of papers, datasets, and reproducible code are made available when possible. For further requests, contact me.

For additional examples of my research, see my CV.

Governance and Ethics of Autonomous and Intelligent Systems

More than 100 public sector, private sector, and non-governmental organizations have published normative AI ethics documents (i.e., codes, frameworks, guidelines, policy strategies) in recent years. Our ongoing empirical study assesses these documents through coding and quantitative and qualitative of 25 ethics topics and 17 policy sectors, resulting in an original open-source data set and analysis of cross-sectoral differences in the prioritization and framing of AI ethics topics.

Schiff, D., Biddle, J., Borenstein, J., & Laas, K. (2020). What’s Next for AI Ethics, Policy, and Governance? A Global Overview. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 153–158.

Schiff, D., Borenstein, J., Laas, K., & Biddle, J. (2021). AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection. IEEE Transactions on Technology and Society. (accepted version) (appendix)

Schiff, D., Laas, K., Biddle, J., & Borenstein, J. (forthcoming 2021). Global AI Ethics Documents: What They Reveal About Motivations, Practices, and Policies. In K. Laas, M. Davis, & E. Hildt (Eds.), Codes of Ethics and Ethical Guidelines, Emerging Technologies, Changing Fields. Springer.

Subcoalition Cluster Analysis

We introduce a novel method for modeling politics in organizations that builds on the model of intra-organizational conflict in March (1962), which we call “subcoalition cluster analysis” (sCCA). The main contribution of sCCA is that it identifies subcoalitions with consistent preferences that are in conflict without placing additional restrictions on the structure of individual preferences. In our paper, we first describe sCCA, emphasizing how it differs from prior clustering and preference aggregation routines. Then, we apply sCCA to two empirical contexts: Wikipedia and the Baseball Writers’ Association of America (BBWAA).

Ganz, S. & Schiff, D. (Under revision). Subcoalition Cluster Analysis: A New Method for Measuring Political Conflict in Organizations. Working paper available at

AI in Government Services

In the context of rising delegation of administrative discretion to advanced technologies, this study aims to quantitatively assess key public values that may be at risk when governments employ automated decision systems (ADS). Drawing on the public value failure framework coupled with experimental methodology, we address the need to measure and compare the salience of three such values—fairness, transparency, and human responsiveness. Based on a preregistered design, we administer a survey experiment to 1460 American adults inspired by prominent ADS applications in child welfare and criminal justice. The results provide clear causal evidence that certain public value failures associated with artificial intelligence have significant negative impacts on citizens’ evaluations of government. We find substantial negative citizen reactions when fairness and transparency are not realized in the implementation of ADS. These results transcend both policy context and political ideology and persist even when respondents are not themselves personally impacted.

Schiff, D. S., Schiff, K. J., & Pierson, P. (2021). Assessing public value failure in government adoption of artificial intelligence. Public Administration. Open access version available at:

The Impact of Automation on Worker Well-being

Discourse surrounding the future of work often treats technological substitution of workers as a cause for concern, but complementarity as a good. However, while automation and artificial intelligence may improve productivity or wages for those who remain employed, they may also have mixed or negative impacts on worker well-being. This study considers five hypothetical channels through which automation may impact worker well-being: influencing worker freedom, sense of meaning, cognitive load, external monitoring, and insecurity. We apply a measure of automation risk to a set of 402 occupations to assess whether automation predicts impacts on worker well-being along the dimensions of job satisfaction, stress, health, and insecurity. Findings based on a 2002-2018 dataset from the General Social Survey reveal that workers facing automation risk appear to experience less stress, but also worse health, and minimal or negative impacts on job satisfaction. These impacts are more concentrated on workers facing the highest levels of automation risk. This article encourages new research directions by revealing important heterogeneous effects of technological complementarity. We recommend that firms, policymakers, and researchers not conceive of technological complementarity as a uniform good, and instead direct more attention to mixed well-being impacts of automation and artificial intelligence on workers.

Nazareno, L. & Schiff, D. (Under revision). The Impact of Automation and Artificial Intelligence on Worker Well-being.

Social Responsibility of Engineering Students

Developing social responsibility attitudes in future engineers and computer scientists is of critical and rising importance.  Yet research shows that prosocial attitudes decline during undergraduate engineering education. We are engaging in study of a wide range of college and pre-college influences and inhibitors, influenced by the Professional Social Responsibility Development Model. Our mixed methods project has resulted in several presentations and one published article, with another paper under development.

Schiff, D. S., Logevall, E., Borenstein, J., Newstetter, W., Potts, C., & Zegura, E. (2020). Linking personal and professional social responsibility development to microethics and macroethics: Observations from early undergraduate education. Journal of Engineering Education, 22.

Schiff, D. S., Lee, J., Borenstein, J., & Zegura, E. (Under review). The impact of community engagement on undergraduate social responsibility attitudes.

AI in Education

Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI’s impacts on education policy and practice have not yet captured the public attention. This paper therefore evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd’s purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socioemotional engagement. Next, in order to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research.

Schiff, D. (2021). Out of the Laboratory and Into the Classroom: The Future of Artificial Intelligence in Education. AI & Society, 36(1), 331–348.

Additional research engages in thematic analysis of 24 national AI policy strategies, reviewing the role of education in global AI policy discourse. It finds that the use of AI in education is largely absent from policy conversations, while the instrumental value of education in supporting an AI-ready workforce and training more AI experts is prioritized. This suggests that AIED and its ethical implications have failed to reach mainstream awareness and the agendas of key decision-makers. In light of this finding, the article considers a typology of five AI ethics principles and proposes ways in which AI policy can better incorporate these concerns in the context of AIED. Finally, the article offers recommendations for AIED scholars towards increasing engagement with ethics and policy-oriented research, policymakers, and ultimately shaping policy deliberations.

Schiff, D. (Under revision). Education for AI, not AI for Education: AI, Education, and Ethics in National AI Policy Strategies. (Pre-print version).

Deepfakes and Misinformation

Scholars have argued that concerns surrounding the impact of misinformation may be overstated. Nevertheless, some politicians’ actions suggest that they perceive a benefit from an informational environment saturated with misinformation (i.e., fake news and deepfakes). To explain this behavior, we argue that strategic and false allegations of misinformation benefit politicians by allowing them to maintain support in the face of information that could be damaging to their reputation. This concept is known as the “liar’s dividend”. We propose that the payoffs from the liar’s dividend work through two theoretical channels: by injecting informational uncertainty into the media environment that upwardly biases evaluations of the politician’s type, or by providing rhetorical cover which supports motivated reasoning by core supporters. To evaluate these potential impacts, we use a survey experiment to randomly assign vignette treatments detailing embarrassing or scandalous information about American politicians to American citizens. Our study design, treatments, outcomes, covariates, estimands, and analysis strategy are described in more detail in our pre-registered analysis plan.

Schiff, K., Schiff, D., and Bueno, N. (Under development). The Liar’s Dividend: The Impact of Deepfakes and Fake News on Politician Support and Trust in Media. Pre-analysis plan available at