Research Papers

Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms

Prerna Ravi, Annalisa Broski, Glenda Stump +3

Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semi-structured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, i.e., there is no globally interpretable way to understand how a model makes estimates. This is a clear problem in policy evaluation applications, particularly in government, because it is difficult to understand whether such models are functioning in ways that are fair, based on the correct interpretation of evidence and transparent enough to allow for accountability if things go wrong. However, there has been little discussion of transparency problems in the causal machine learning literature and how these might be overcome. This paper explores why transparency issues are a problem for causal machine learning in public policy evaluation applications and considers ways these problems might be addressed through explainable AI tools and by simplifying models in line with interpretable AI principles. It then applies these ideas to a case-study using a causal forest model to estimate conditional average treatment effects for a hypothetical change in the school leaving age in Australia. It shows that existing tools for understanding black-box predictive models are poorly suited to causal machine learning and that simplifying the model to make it interpretable leads to an unacceptable increase in error (in this application). It concludes that new tools are needed to properly understand causal machine learning models and the algorithms that fit them.

This research investigates road traffic accident severity in the UK, using a combination of machine learning, econometric, and statistical methods on historical data. We employed various techniques, including correlation analysis, regression models, GMM for error term issues, and time-series forecasting with VAR and ARIMA models. Our approach outperforms naive forecasting with an MASE of 0.800 and ME of -73.80. We also built a random forest classifier with 73% precision, 78% recall, and a 73% F1-score. Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for Explainable AI, highlighting influential factors like Driver_Home_Area_Type and Road_Type. Our study enhances understanding of accident severity and offers insights for evidence-based road safety policies.

While certain industrial sectors (e.g., aviation) have a long history of mandatory incident reporting complete with analytical findings, the practice of artificial intelligence (AI) safety benefits from no such mandate and thus analyses must be performed on publicly known ``open source'' AI incidents. Although the exact causes of AI incidents are seldom known by outsiders, this work demonstrates how to apply expert knowledge on the population of incidents in the AI Incident Database (AIID) to infer the potential and likely technical causative factors that contribute to reported failures and harms. We present early work on a taxonomic system that covers a cascade of interrelated incident factors, from system goals (nearly always known) to methods / technologies (knowable in many cases) and technical failure causes (subject to expert analysis) of the implicated systems. We pair this ontology structure with a comprehensive classification workflow that leverages expert knowledge and community feedback, resulting in taxonomic annotations grounded by incident data and human expertise.

Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measurements on quantum circuits introduce probabilistic errors which impact the convergence of these methods. On the other hand, the phase space of a quantum circuit expands exponentially with the number of qubits, complicating efforts to execute xAI methods in polynomial time. In this paper we will discuss the performance of established xAI methods, such as Baseline SHAP and Integrated Gradients. Using the internal mechanics of PQCs we study ways to speed up their computation.

There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the internet of things, affecting data privacy. Existing frameworks have identified the need to focus on human-centered design, combining technical and business/organizational perspectives. However, trust remains a critical issue that needs to be designed from the beginning. The proposed framework expands from the human-centered design approach, emphasizing and maintaining the trust that underpins the process. This paper proposes a theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business technology approach for the agile co-creation process. The aim is to streamline the adoption process of AI to innovate and improve business by involving all stakeholders throughout the project so that the AI technology is designed, developed, and deployed in conjunction with people and not in isolation. The framework presents a fresh viewpoint on responsible AI implementation based on analytical literature review, conceptual framework design, and practitioners' mediating expertise. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI. This human-centered approach is aligned with and enabled by the privacy by design principle. The creators of the technology and the end-users are working together to tailor the AI solution specifically for the business requirements and human characteristics. An illustrative case study on adopting AI for assisting planning in a hospital will demonstrate that the proposed framework applies to real-life applications.

Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we expect explanations to remain objective or apolitical. Our functionalist understanding of these models is of less advantage than we might assume. Models precede explanations, and can be useful even when both model and explanation are incorrect. Explainability may never win the race against complexity, but this is less problematic than it seems.

Fairness Implications of Encoding Protected Categorical Attributes

Carlos Mougan, Jose M. Alvarez, Salvatore Ruggieri +1

Past research has demonstrated that the explicit use of protected attributes in machine learning can improve both performance and fairness. Many machine learning algorithms, however, cannot directly process categorical attributes, such as country of birth or ethnicity. Because protected attributes frequently are categorical, they must be encoded as features that can be input to a chosen machine learning algorithm, e.g.\ support vector machines, gradient boosting decision trees or linear models. Thereby, encoding methods influence how and what the machine learning algorithm will learn, affecting model performance and fairness. This work compares the accuracy and fairness implications of the two most well-known encoding methods: \emph{one-hot encoding} and \emph{target encoding}. We distinguish between two types of induced bias that may arise from these encoding methods and may lead to unfair models. The first type, \textit{irreducible bias}, is due to direct group category discrimination, and the second type, \textit{reducible bias}, is due to the large variance in statistically underrepresented groups. We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness. Furthermore, we consider the problem of intersectional unfairness that may arise when machine learning best practices improve performance measures by encoding several categorical attributes into a high-cardinality feature.

Explanation as a process: user-centric construction of multi-level and multi-modal explanations

Bettina Finzel, David E. Tafler, Stephan Scheele +1

In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to different contexts and requirements of stakeholders. Explanations must not only suit developers of models, but also domain experts as well as end users. Thus, in order to satisfy different stakeholders, explanation methods need to be combined. While multi-modal explanations have been used to make model predictions more transparent, less research has focused on treating explanation as a process, where users can ask for information according to the level of understanding gained at a certain point in time. Consequently, an opportunity to explore explanations on different levels of abstraction should be provided besides multi-modal explanations. We present a process-based approach that combines multi-level and multi-modal explanations. The user can ask for textual explanations or visualizations through conversational interaction in a drill-down manner. We use Inductive Logic Programming, an interpretable machine learning approach, to learn a comprehensible model. Further, we present an algorithm that creates an explanatory tree for each example for which a classifier decision is to be explained. The explanatory tree can be navigated by the user to get answers of different levels of detail. We provide a proof-of-concept implementation for concepts induced from a semantic net about living beings.

Measurement of social phenomena is everywhere, unavoidably, in sociotechnical systems. This is not (only) an academic point: Fairness-related harms emerge when there is a mismatch in the measurement process between the thing we purport to be measuring and the thing we actually measure. However, the measurement process -- where social, cultural, and political values are implicitly encoded in sociotechnical systems -- is almost always obscured. Furthermore, this obscured process is where important governance decisions are encoded: governance about which systems are fair, which individuals belong in which categories, and so on. We can then use the language of measurement, and the tools of construct validity and reliability, to uncover hidden governance decisions. In particular, we highlight two types of construct validity, content validity and consequential validity, that are useful to elicit and characterize the feedback loops between the measurement, social construction, and enforcement of social categories. We then explore the constructs of fairness, robustness, and responsibility in the context of governance in and for responsible AI. Together, these perspectives help us unpack how measurement acts as a hidden governance process in sociotechnical systems. Understanding measurement as governance supports a richer understanding of the governance processes already happening in AI -- responsible or otherwise -- revealing paths to more effective interventions.

Modelling GDPR-Compliant Explanations for Trustworthy AI

Francesco Sovrano, Fabio Vitali, Monica Palmirani

Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision- Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM into the identification of an appropriate path over an Explanatory Space, allowing explainees to interactively explore it and produce the explanation best suited to their needs. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.

This article examines the concept of 'AI fairness' for people with disabilities from the perspective of data protection and equality law. This examination demonstrates that there is a need for a distinctive approach to AI fairness that is fundamentally different to that used for other protected characteristics, due to the different ways in which discrimination and data protection law applies in respect of Disability. We articulate this new agenda for AI fairness for people with disabilities, explaining how combining data protection and equality law creates new opportunities for disabled people's organisations and assistive technology researchers alike to shape the use of AI, as well as to challenge potential harmful uses.