Enterprise AI
January 3, 2024

Developing AI Responsibly for Diversity and Inclusion: A Playbook with Sharifah Amirah

Sharifah Amirah shares a playbook for developing AI responsibly to promote diversity and inclusion by building diverse teams, fostering readiness, and deconstructing biases.
Written by Ankur Patel
This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 2,000+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.

New AI tools seem full of promise for fixing workplace diversity problems. But they also bake in human biases that can make inclusion worse. So how do leaders tap the good while protecting against the bad?

After 20 years helping global banks, tech titans and startups do data right, IntentHQ’s Chief Product Officer, Sharifah Amirah, sat down with us in our latest episode of Pioneers to share her battle-tested tips. Her wisdom offers a simple leadership game plan for using AI to thoughtfully boost equity.

Before we dive into all that Sharifah shared, be sure to check out the full episode here:

People and Outlook Guide Purposeful AI

Any AI lives or dies based on the hands shaping it. Who’s around the design table? What questions do they ask? Which ideas move ahead? Even proven apps link back to human choices made long before launch.

Leaders seeking AI that drives inclusion must purposefully build creative groups with different thinkers raising shared questions. Embracing unexpected voices surfaces hidden issues and sparks unforeseen solutions worth betting on.

Varied minds break groups out of comfy ruts where people like each other pontificate without questioning assumptions. Diverse perspectives pave the path for AI assisting excluded groups.

AI's Foundations Reveal It’s Failings and Future

Diversity in viewpoint serves as a key ingredient for AI furthering diversity itself. But equal inclusion in process and power remains critical for avoiding uneven effects.

Sharifah reveals how early recruiting algorithms at tech titans actually just perfected the replication of narrow employee prototypes (i.e. privileged white men from elite colleges). This again exposes AI's foundations. Systems optimizing historical practices, however harmful, require intentional intervention to propel new paradigm progress. Leaders must pursue "next" practices shaped by wide participation, not just discourage detrimental behaviors within lingering inequitable systems.

Doing differently means moving from tolerance toward total belonging and jointly creating what's next. Out-of-the-box AI boosts those already winning. But thoughtfully constructed, AI can overhaul systems so more people can meaningfully compete and prosper.

Readying Culture and Partners for Responsible AI

Even well-intended AI flops without cultural readiness. So leaders must carefully nurture an appetite for safe innovation through habits and ecosystems before productively applying inclusive AI.

At data trailblazer IntentHQ, this means deep transparency and a regular collective review of existing dynamics. Self-reflection reveals blindspots while sparking ideas on possibilities. And this receptive culture intentionally spreads further through partners collaborating on AI guardrails.

Companionably investigating values and behaviors provides direction for AI usage stretching ethically across entire business ecosystems. Doing right expands through connections, not isolation.

Deconstructing AI's Black Box Blindspots

With receptive cultures primed for responsible AI advancement, unraveling conventional code driving systems represents the next frontier. And neglecting strict scrutiny exponentially expands discrimination through an accountability vacuum.

But leaders must insist technology creators open the black box illuminating AI's secret sauce. Bright light and hard questions best disinfect bias. Dig into what training data got used, what inaccuracies got dismissed, and what yardsticks determine success. Don't blithely accept metrics merely maintaining unfair status quos reflecting historical privilege for some over others.

Stay unsatisfied with value-neutral talk or altruistic happy talk. Demand simplicity on how AI gets built and chosen as helpful versus harmful:

  • What worldview drove framing of solutions judged appropriate?
  • How did we fill gaps in limited, biased data plaguing systems?
  • Did a diversity of stakeholders participate in shaping pilots or defining success?

Leaders will grow into stronger question-askers through exposure, further spurring inclusive evolution. But set clear transparency expectations rather than reacting to undesirable impacts that become impossible to undo.

Poisonous Past and Promising Pathways Forward

We’re still at sunrise for AI expanding opportunity and equity. While missteps litter early days, consistent differences arise from diverse teams openly building applications tackling society’s biggest quandaries.

The future remains undetermined. But leaders constructing creative groups to intently interrogate black box AI substantially shape what opportunities tomorrow holds for whom. We must all lean into that duty.

So bringing together mixed minds to deeply question how AI gets built and used represents the first phase in seizing technology’s promise while avoiding its pitfalls. That grounds lasting progress.

What questions on employing AI to tackle inclusion challenges currently captivate your curiosity? Start small but solidly to position expanding AI as an accelerating tool for equity as capabilities advance. Match high tech with high trust teams to unleash AI’s multiplier effects for shared returns long into the future.

Want to learn more about responsible AI adoption? Check out this episode on building trust in AI with Jim Beech, CEO of Direct Mortgage Corp.

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