# Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions

arXiv: https://arxiv.org/abs/2401.04118 · HTML: https://mlsanity.com/paper/2401.04118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems](https://mlsanity.com/paper/2501.01441/) (2024)
- [An Explanatory Model Steering System for Collaboration between Domain Experts and AI](https://mlsanity.com/paper/2405.13038/) (2024)
- [Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users](https://mlsanity.com/paper/2504.13897/) (2025)
- [Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions](https://mlsanity.com/paper/2505.20312/) (2025)
- [Explainability-in-Action: Enabling Expressive Manipulation and Tacit Understanding by Bending Diffusion Models in ComfyUI](https://mlsanity.com/paper/2508.07183/) (2025)
- [Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability](https://mlsanity.com/paper/2607.22428/) (2026)

## Abstract (arXiv)

With Artificial Intelligence (AI) becoming ubiquitous in every application domain, the need for explanations is paramount to enhance transparency and trust among non-technical users. Despite the potential shown by Explainable AI (XAI) for enhancing understanding of complex AI systems, most XAI methods are designed for technical AI experts rather than non-technical consumers. Consequently, such explanations are overwhelmingly complex and seldom guide users in achieving their desired predicted outcomes. This paper presents ongoing research for crafting XAI systems tailored to guide users in achieving desired outcomes through improved human-AI interactions. This paper highlights the research objectives and methods, key takeaways and implications learned from user studies. It outlines open questions and challenges for enhanced human-AI collaboration, which the author aims to address in future work.