We live in a world that is increasingly dominated by Artificial Intelligence (AI), the technology expected to revolutionize almost every field, including the corporate legal sector. AI’s powerful predictive abilities have the potential to be a great boon, but ambiguity about how these algorithms operate (often referred to as the ‘black box’ conundrum) has stirred debates regarding their explainability and propriety.
AI’s workings are often opaque even to those who develop and train AI models. The lack of transparency in AI’s complex decision-making mechanisms has raised concerns about potential biases that may slink into the processes. It’s crucial to note how these potential biases can affect sectors like healthcare, criminal justice, credit scoring, and hiring processes, where decisions based on such AI models largely influence people’s lives.
Despite the cloud surrounding the topic, the demystification of AI’s workings is not an impossibility. Legal practitioners, armed with a basic understanding of AI, should strive to advocate for transparency and accountability in AI’s functions. They play a vital role in ensuring that AI solutions are unbiased and adhere to the principles of fairness and neutrality.
Here are seven actionable strategies for lawyers to promote explainable AI:
- Embrace The Challenge Of Explainable AI: Consider the black box metaphor as a call to action for promoting greater transparency and accountability within AI systems’ operations.
- Learn The Basics Of AI: Familiarize yourself with fundamental AI concepts and ethical concerns so as to better explain AI’s workings to clients and regulators. There’s a wide variety of resources and strategies at your disposal to help you to learn more about AI.
- Collaborate With Technology And AI Specialists: Close collaboration with AI developers and tech experts can aid in the understanding of the key drivers behind AI decisions. Their input can also help set clear expectations around transparency, reliability, and fairness.
- Prove AI’s Validity And Fairness: While it might not be feasible to view the inner workings of an AI model, evaluating its outcomes based on rigorous testing and validation can help identify and mitigate potential biases. This creates a body of evidence for AI’s fairness and efficacy.
- Standardize AI Reporting: Develop standardized formats for reporting AI outputs to enhance clarity and cross-communication.
- Prioritize Transparent AI Models: Make it a standard practice to use transparent AI models that provide clearer explanations of their decision-making processes.
- Insist On Transparency From Vendors: Question vendors about the mechanics of their tools and insist on details about how their systems make decisions and process data.
Adopting these proactive measures can help to promote a more inclusive and fair use of AI technologies, thereby fostering a brighter, more accountable, and transparent future for AI.