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Found 6 papers, 2 papers with code
Humble AI in the real-world: the case of algorithmic hiring
Humble AI (Knowles et al., 2023) argues for cautiousness in AI development and deployments through scepticism (accounting for limitations of statistical learning), curiosity (accounting for unexpected outcomes), and commitment (accounting for multifaceted values beyond performance).
Granite Guardian
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM).
Usage Governance Advisor: From Intent to AI Governance
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them.
AutoDOViz: Human-Centered Automation for Decision Optimization
AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.
IRF: Interactive Recommendation through Dialogue
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
