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. https://doi.org/10.1145/3375627.3375804
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. https://doi.org/10.1109/TTS.2021.3052127 (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 https://osf.io/preprints/socarxiv/5kufg/.
AI in Government Services
This study aims to quantitatively assess public value failures associated with government use of automated decision systems (ADS). Based on a pre-registered design, we administer a survey experiment to 1,460 American adults that draws on prominent ADS use cases in child welfare and criminal justice. The results show 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 the public values of 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., Schiff, K. J., & Pierson, P. (Under revision). Public Value Failure in Government Adoption of AI. Pre-analysis plan available at https://osf.io/neu3j/
The Impact of Automation on Worker Well-being
Discourse surrounding the Fourth Industrial Revolution often treats technological substitution of workers as a cause for concern, but complementarity as a good. However, while automation and artificial intelligence may improve efficiency or wages for those who remain employed, they may also have mixed or negative impacts on worker well-being. Increased uptake of automation in work environments may affect worker autonomy, cognitive load, socialization, job insecurity, and external monitoring, among other effects. This study considers several hypothetical channels through which automation may impact worker well-being. We combine two different automation risk measures with a set of occupation codes to assess whether automation risk predicts impacts on job satisfaction, stress, and health.
Nazareno, L. & Schiff, D. (Working paper). 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 paper under review. Another paper is 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. https://doi.org/10.1002/jee.20371
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. (2020). Out of the Laboratory and Into the Classroom: The Future of AI in Education. AI & Society. https://doi.org/10.1007/s00146-020-01033-8.
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 http://egap.org/registration/6435.