Many enterprises aim to infuse AI throughout their business. However, users often mistrust or resist the technology. This impedes adoption of even the most accurate systems. Organizations must proactively build user trust in AI by focusing on more than just system accuracy.

The Limitations of Accuracy

Most AI teams focus intensely on maximizing model accuracy. They assume accurate systems naturally engender user trust. However, research shows trust depends on more than accuracy:

  • Explainability – Users distrust systems that act as “black boxes”. Lack of transparency into AI reasoning undermines adoption.
  • Perceived fairness – Even accurate AIs can make logically sound but counterintuitive decisions. Humans question outcomes that appear subjectively unfair.
  • User control – Systems that allow zero user overrides can frustrate users. People need some degree of control over technology.
  • Familiarity – Users have more inherent trust in AI if they understand how it works. Unfamiliar systems cause inherent skepticism.

High accuracy is not sufficient. User trust emerges from a combination of transparency, perceived fairness, user control, and general familiarity with AI.

Driving Transparency

Enable users to look under the hood at AI decision-making:

  • Explain overall system logic at a high level so users understand the general approach rather than just outputs.
  • Provide specific explanations for individual decisions when needed to clarify the reasoning behind a particular AI judgment.
  • Leverage techniques like LIME that can reverse engineer model logic to explain specific predictions.
  • Compare AI decisions to what a human expert would decide, to highlight the similarities and differences in reasoning.

Transparency builds familiarity with the technology while also ensuring users feel the system behaves fairly even on counterintuitive conclusions.

Enabling User Control

Allowing some user overrides maintains trust in the overall AI:

  • For high-stakes decisions, keep humans in the loop to approve recommendations. This sustains user agency.
  • Provide optional modes where users can tweak model thresholds and constraints to influence outputs.
  • Enable user feedback loops to improve model reasoning in areas where users perceive unfairness or inaccuracy.
  • Offer opt-out mechanisms for users strongly opposed to AI involvement in certain decisions.

Control assures users they can influence and enhance the system when needed. This grants them agency over the technology rather than being passive subjects.

Securing Buy-In across Roles

Different internal roles have varying concerns about AI adoption. Addressing their unique needs secures buy-in:

  • Leadership wants reputational assurances and clear ROI. Assuage concerns about public perception andbiased AI. Demonstrate a pilot’s hard benefits before scaling.
  • Operations wants reliability. Rigorously monitor and test AI in lower-stakes applications first. Document processes for managing failures.
  • Legal wants compliance. Catalogue applicable regulations and restrictions. Document processes for transparency and user control.
  • Users want usability. Co-design interfaces alongside staff. Solicit continuous feedback during rollout to streamline adoption.

Multipronged efforts spanning accuracy, transparency, control and buy-in help users across the organization become advocates rather than adversaries of AI. While challenging, responsible AI adoption reaps immense rewards.