The Role of Explainable AI in the Research Field of AI Ethics

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research fields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review offers an opportunity to detect research gaps and focus points. This paper presents the results of a systematic mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This paper contributes to the research field with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the field of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are reflected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics the collected dataset opens possibilities to continue the mapping process in other directions.© 2023 Association for Computing Machinery.fi=vertaisarvioitu|en=peerReviewed

Agent-Based Modeling of Resilience in Smallholder Agriculture: Toward Robust Models and Equitable Outcomes

Smallholder farmers constitute one of the world's most vulnerable populations. Moreover, rising socioeconomic inequalities and biophysical degradation threaten to increase this vulnerability. There is therefore a pressing need to build resilience in smallholder agriculture. Socio-environmental systems (SES) modeling can support this goal, yet confronts two challenges that may limit its usefulness for informing agricultural development. First, as agricultural systems are highly heterogeneous and our ability to model them is imperfect, there is a risk that model-based recommendations inadvertently increase vulnerability. Second, there exist a range of approaches to agricultural development that prioritize distinct objectives (e.g., market integration versus social equity), and conflicts between these approaches could undermine progress toward more resilient futures. To build smallholder resilience therefore requires an integrated perspective on development as well as robust methodologies for comparing and integrating alternative development strategies. This dissertation uses agent-based modeling (ABM) to help address these challenges. The first contribution of this dissertation is a set of methodological advances that improve the robustness of model-based policy analysis. These advances question two analytical norms within SES modeling. The first is a lack of attention to equity. For instance, by disregarding heterogeneity in outcomes, model-based recommendations may benefit the well-off at the expense of the vulnerable and thereby perpetuate inequity. Chapters two and three address this issue, first by establishing a conceptual framework for the equity-ABM interface and then by applying an agent-based model to examine equity in the effects of resilience-enhancing strategies. The second analytical norm that this dissertation questions is the use of a single, “best-fit” model to assess policy effects; due to our incomplete understanding of complex SES, multiple plausible models may exist. This common condition is known as equifinality, but it is not often considered in SES modeling or policy analysis. To attend to this challenge, chapter four develops an approach for identifying a set of diverse model calibrations and using these to achieve a more robust policy analysis. Together, these methodological advances facilitate more robust and equitable policy assessments, in agricultural systems and beyond. The second principal contribution of this dissertation is substantive. Emerging from the modeling of smallholder resilience, I find complementarity between disparate agricultural development approaches. For instance, chapter five compares the effects of legume cover cropping (a form of ecological farm management) and microinsurance (a financial institutional support) on smallholder climate resilience. Although these approaches are traditionally promoted by distinct academic communities and development organizations, the results show that, when implemented together, they are highly complementary. Next, chapter six investigates the potential for contract farming to overcome the negative effects of large-scale land acquisitions on smallholder food security. Results suggest that preserving smallholder autonomy through contract farming can simultaneously improve smallholder food security and agricultural production, thereby better aligning the preferences of developers and smallholders. Thus, these chapters together suggest the benefits of reconciling perspectives on and approaches to agricultural development. As a whole, this dissertation advances the application of agent-based modeling and resilience thinking in smallholder agriculture. Beyond agricultural applications, it lays the groundwork for identifying robust and equitable development strategies in SES.PhDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169916/1/tgw_1.pd