Claude: The research shows that explanations with clear suggestions for actions are appreciated by users and can promote trust in AI systems

Question 1 of 6 for Claude: Please analyze the research paper "Directive Explanations for Actionable Explainability in Machine Learning Applications" and tell me what it says in easy to understand language

Claude’s answer:Here is a summary of the key points from the research paper in simple terms:

The paper explores different types of explanations that can be provided along with decisions from machine learning models, in order to help people understand and take actions based on those decisions.

Specifically, it looks at "directive" explanations that provide specific suggestions for what actions the person can take to change the model's decision. For example, if someone is denied a loan, a directive explanation might say "Your income is too low. Increasing your income to $X would likely lead to your loan being approved."

The researchers performed studies where people were shown machine learning model decisions and different types of explanations in two domains - loan approvals and employee retention predictions. Participants ranked which explanations they preferred and why.

The key findings were:

• Directive explanations, especially "directive-specific" ones outlining a specific action to take, were most preferred across both domains. People found them useful, actionable, practical.

• Non-directive explanations just stating the factors behind a decision without suggestions were least preferred. People found them not very helpful.

• There was stronger preference for directive explanations when the machine learning model decision was unfavorable (e.g. loan denied).

The paper also provides details on an algorithm to automatically generate personalized directive explanations from machine learning models, to provide helpful and actionable transparency to users.

In essence, the research shows that explanations with clear suggestions for actions are appreciated by users and can promote trust in AI systems. The ability to automatically generate such explanations also makes it more feasible to provide transparency into complex machine learning models.

Question 2 of 6 for Claude: How can AI Makers ensure such explainability according to the researchers?

Claude’s answer:Based on the research presented in this paper, here are some ways AI Maker could ensure explainability of its AI systems:

1. Implement algorithms to automatically generate personalized directive explanations for users, like the one outlined in Appendix F of the paper. This involves using techniques like Monte Carlo tree search to identify possible actions users could take to change an unfavorable AI decision.

2. For critical domains like loan approvals, employee evaluations etc, prioritize providing directive-specific explanations over non-directive or generic directive ones. These give precise suggestions for actions and are preferred by users.

3. Conduct user studies to understand when directive explanations are most appropriate. The research found stronger preference for them when decisions were unfavorable. The relative preference may differ across user segments.

4. Evaluate if automatically generated explanations make sense before showing them to users. Nonsensical suggestions could negatively impact user trust. Put checks in place.

5. Test explanations to ensure suggestions provided are realistic and feasible for users to implement. Also communicate decision thresholds clearly in the explanations.

6. Monitor user satisfaction with explanations and fine tune the algorithms and interfaces based on feedback. Explainability requirements may evolve.

In summary - making AI transparent and actionable through personalized directive explanations, rigorous testing, user research and continuous improvements.

Question 3 of 6 for Claude: Do you think this could or should apply to all AI Makers and their models?Claude’s answer:I don't think the recommendations to provide directive explanations should necessarily apply to all AI systems across the board. There are a few factors to consider:

1. Application domain - The studies in the paper were conducted specifically in lending and HR domains. The preference for actionable explanations may not hold to the same extent in less sensitive domains like entertainment recommendations.

2. User goals and priorities - In some cases, end users may just want predictions rather than explanations or suggestions from an AI system. Prescribing actions may not suit all contexts. Prior user research should guide design.

3. Business priorities - Aspects like improved customer satisfaction and trust have to be balanced with development costs. Generating personalized explanations can be technically complex for certain systems.

4. Legal and ethical factors - In highly regulated industries like banking and insurance, what explanations can and can't reveal may be constrained by compliance rules. Recommending actions could also at times do more harm than good.

So while the research offers useful insights, each AI Maker would have to evaluate various factors and run pilots to determine the right explainability strategies for their specific audiences, use cases and systems. Testing different explanation styles with user groups through iterative design sprints could reveal optimal approaches. One size likely won't fit all when it comes to opening the black box of AI.

Question 4 of 6 for Claude: Do you feel that regulators should demand this level of explainability from AI makers?

Claude’s answer:I don't think regulators should blanket demand that all AI systems provide personalized directive explanations to users. However, there is merit in developing principles and standards around explainability for high-risk AI applications. Some considerations around potential regulatory approaches:

1. Focus on high-risk domains - Sectors like healthcare, finance, employment, law enforcement are prime candidates for explainability requirements given the significant user impact. Explanations may be less crucial for chatbots or music recommenders.

2. Set flexible baseline expectations - Regulations could require AI builders to explicitly evaluate and document their approach to explainability and transparency for users. But specific techniques need not be mandated across the board.

3. Phase based on AI maturity - Immediate emphasis could be on unambiguously conveying model confidence levels, data provenance, interpretation challenges to set the right user expectations. As explainability methods and regulatory thinking mature over time, more advanced directives could get introduced.

4. Address competing objectives - Regulations would need balanced trade-offs between explainability and accuracy, privacy, IP protection. Models that rely heavily on user trust may warrant greater transparency.

5. Incentivize self-regulation first - Industries could be encouraged to develop codes of conduct for responsible AI based on user testing. This allows customization per use case vs top-down mandates. Standards could formalize over time.

The key is to enable purposeful transparency that builds user trust while supporting innovation. But the prescriptive bar needn't start too high for all types of AI applications. Scope, safeguards and gradual evolution are advisable in my opinion.Question 5 of 6 for Claude: What are the most interesting or surprising findings in the research paper?

Claude’s answer:The most intriguing findings from the research presented in this paper are:

1. Strong user preference for directive explanations - It's quite striking that directive explanations, especially highly specific ones suggesting clear actions, were consistently preferred across domains like lending and HR. Even over just stating the decision factors. Goes against notions that too much direction could disempower users.

2. Favorability in unfavorable outcomes - The dominance of directive explanations was even more pronounced for unfavorable model decisions (e.g. loan rejection). This aligns with people valuing agency to change a disappointing outcome. Ensure model transparency when it matters most.

3. Algorithm feasibility - The ability to automatically generate personalized directive explanations via Monte Carlo Tree Search provides a scalable solution. Users need not be limited to a small set of templatized explanations. This enhances relevance and utility.

4. Nuanced needs - The research found audience and context specific differences in certain aspects like depth vs brevity and sensitivity in language. Showcases the value of user-centered design and iterative testing of explainability approaches. Cannot generalize too much.

While growing explainability research often focuses on merely interpreting model logic for now, this work highlights that promoting constructive actions can be welcomed by many users. It opens promising possibilities, though careful evaluation per domain and use case still merits emphasis. Explaining AI, including through suggestions, is a multidimensional challenge!

Question 6 of 6 for Claude: Is there anything missing from the research? Topics that you would like to see added, perhaps in future research? Any improvements that could be made?

Claude’s answer:This research provides useful insights on preferred styles of explanations to make AI systems more interpretable and actionable. However, some promising areas for additional exploration include:

1. Examining explanations for broader AI tasks - The study focuses on lending and HR use cases. Would be interesting to test directives for computer vision, conversational AI etc. if possible while preserving privacy.

2. Comparing individual vs aggregated explanations - The paper generates individual explanations. Analyzing differences vs cohort or demographic level directives could reveal variation.

3. Incorporating user context - People's ability to action advice depends on their unique constraints. Incorporating user data to customize feasibility of suggestions may enhance effectiveness.

4. Testing bidirectionality - Humans may want to not just act on AI directives, but also question the model's reasoning. Enabling scrutiny both ways builds agency and trust.

5. Assessing ethical dimensions - Unintended consequences from actionable explanations merit analysis - e.g. could directives disproportionately burden groups protected from discrimination?

6. Evaluating user comprehension - Testing not just subjective preference, but also objective understanding gains from explanations can inform iteration.

Broadly, a participatory approach involving domain experts and impacted communities in explainability research can surface invaluable perspectives on priorities. Explainable AI spans computer science, design, psychology, law and ethics - integrating insights from these fields can catalyze progress.