Lawyers Who Align AI With Client Values Blaze a Meaningful Path

Artificial intelligence has emerged as a transformative force, rapidly advancing its own capabilities and enabling industries to become more automated, data-driven, and technology-enabled.

As AI remains in focus, so has the concept of centering client needs with AI solutions. Harnessing AI’s immense potential means aligning its development and application with the specific goals and values of clients who will be using it.

This ensures AI solutions deliver targeted benefits in an ethical, sustainable, and client-centric manner.

Beyond Efficiency

Clients adopt AI primarily to enhance efficiency, reduce costs, and gain a competitive edge. Walmart, for instance, has leveraged AI to optimize its truck routes, saving millions of dollars in annual fuel costs.

Similarly, JPMorgan Chase’s COIN platform has automated 360,000 hours of manual work annually through machine learning, while Netflix uses AI to offer ultra-precise movie recommendations to engage subscribers.

While efficiency and cost reduction are compelling benefits, aligning AI with client values is equally important. Clients increasingly are prioritizing sustainability, social responsibility, and transparency in their operations.

Unilever, a global consumer goods giant, has embedded AI into its factories to minimize waste generation, demonstrating its commitment to eco-friendly practices. The UK National Health Service, recognizing the potential for bias in AI algorithms, developed a diabetic eye disease screening tool that openly informs users of potential biases, promoting transparency and trust.

Pitfalls of Misaligned AI

When AI isn’t aligned with client goals and values, it can lead to problems. AI recruitment tools might incorporate biases against certain types of individuals, and concerns have been raised over Facebook’s AI moderation systems, causing an uproar among users and raising concerns about potential censorship and manipulation.

AI developers can build client-centric solutions by fostering open dialogue and collaboration to fully understand client goals and values.

Toyota, a pioneer in the automotive industry, took this approach to heart. Before developing AI robots for its assembly lines, Toyota employees spent more than a year observing and understanding the intricate processes and nuances of human workers.

This deep understanding enabled them to create AI solutions that enhanced the skills and capabilities of their human counterparts, rather than replacing them.

Collaborative, Adaptive Culture

Integrating client perspectives into every stage of AI development is crucial. To ensure its anti-discrimination algorithm reflected community standards, Airbnb designed simulations based on real user data, allowing the company to identify and mitigate potential biases before deploying the algorithm.

Multidisciplinary teams also play a vital role in considering problems holistically, and ensuring AI solutions align with a wider range of client needs. Microsoft’s Healthcare NExT initiative, which aims to develop AI tools for health-care applications such as cancer screening and triage coordination, exemplifies this approach.

By working cross-functionally, the team could anticipate and address potential blind spots in the AI models, ensuring their relevance and effectiveness in real-world health-care settings.

Rapid iteration and feedback channels are also essential for tuning AI solutions to specific client requirements. Uber uses explainable machine learning models to dynamically adjust ride pricing based on real-time supply-and-demand dynamics.

This transparency allows both drivers and riders to understand the factors influencing pricing decisions, fostering trust and enhancing overall user experience.

Open-source initiatives, such as OpenAI’s publication of the entirety of its code for GPT-3’s natural language capabilities, further promote transparency and accountability. By making AI models and algorithms accessible to the broader research community, companies demonstrate their commitment to ethical AI development and open collaboration.

Balancing Constraints and Aspirations

While aligning AI with client goals and values is paramount, it’s important to acknowledge inherent challenges of balancing technical constraints and client aspirations. AI development is often a complex and iterative process, and achieving desired outcomes may require compromises and adjustments.

However, thoughtful collaboration, open communication, and a focus on real-world human impact can bridge the gap between technical feasibility and client expectations.

By working together, AI developers and clients can navigate the complexities of AI development and unleash its immense potential to drive meaningful progress across industries.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law and Bloomberg Tax, or its owners.

Author Information

Colin S. Levy is a lawyer and director of legal at Malbek.

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