arXiv subjects

Larry Chan

Publications and source records attributed to Larry Chan.

4 recordsLinked to original sources

arXiv2021

The Who in XAI: How AI Background Shapes Perceptions of AI Explanations

Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.

cs.HC↗

arXiv2019

Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.

cs.AI↗

arXiv2017

The View from the Other Side: The Border Between Controversial Speech and Harassment on Kotaku in Action

In this paper, we use mixed methods to study a controversial Internet site: The Kotaku in Action (KiA) subreddit. Members of KiA are part of GamerGate, a distributed social movement. We present an emic account of what takes place on KiA who are they, what are their goals and beliefs, and what rules do they follow. Members of GamerGate in general and KiA in particular have often been accused of harassment. However, KiA site policies explicitly prohibit such behavior, and members insist that they have been falsely accused. Underlying the controversy over whether KiA supports harassment is a complex disagreement about what "harassment" is, and where to draw the line between freedom of expression and censorship. We propose a model that characterizes perceptions of controversial speech, dividing it into four categories: criticism, insult, public shaming, and harassment. We also discuss design solutions that address the challenges of moderating harassment without impinging on free speech, and communicating across different ideologies.

cs.OH↗

arXiv2017

Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations

We introduce AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior. We describe a rationalization technique that uses neural machine translation to translate internal state-action representations of an autonomous agent into natural language. We evaluate our technique in the Frogger game environment, training an autonomous game playing agent to rationalize its action choices using natural language. A natural language training corpus is collected from human players thinking out loud as they play the game. We motivate the use of rationalization as an approach to explanation generation and show the results of two experiments evaluating the effectiveness of rationalization. Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.

cs.AI↗