Editorial
First, let’s all take a collective deep breath. The growing use of artificial intelligence (AI) and how humans will collaborate with it has been a future of work trend for years now. It's only since the launch of ChatGPT that the world realized the fundamental changes this rapidly advancing technology would have on our lives and our work.
Most of us are unaware of how often we interact with AI. AI is in hiring systems, judicial systems, the media we consume and the products we use. And we already know that despite the promise of AI eliminating human errors and biases, AI can have or learn bias. Algorithms are only as inclusive as the people who create and train them.
Several years ago, I forecasted that AI would augment and automate certain tasks and jobs, shifting workplaces to be more focused on human-machine collaboration — optimized teams of people and programs together making work more efficient and productive. A recent report indicated that nearly 80% of jobs will be impacted by generative AI, like GPT models. The rapid advancement of generative AI means that many jobs in creative and STEM fields I thought were at low risk for automation will now see far-reaching impacts.
Even though it’s not a doom-and-gloom, “machines are taking all of our jobs” scenario, the workplace and the jobs of today will continue to change. To prepare humans to effectively manage the AI behemoth, new skills are needed in new roles, and it is critical that we have equitable access to reskilling programs.
Capitalizing on the promise of AI while minimizing the pitfalls is a DEI imperative. Here's how we get there.
Understand How Your AI Tools Were Created, Trained and Monitored for Bias
When evaluating generative AI and other automated tools, companies and leaders should look for and adopt products that transparently address bias. Before you buy, ask for representation and/or demographic data of the AI development teams; how varied and inclusive the training data sets were; and how they monitor, audit and address bias when it arises.
Without established processes for reporting and monitoring, these biases can go unquestioned and then be reinforced, especially as the models are continually learning and refining their responses based on user prompts.
Monitoring for bias is important for building more inclusive workplaces, as well as navigating legal and regulatory implications for companies. The United States Equal Employment Opportunity Commission recently updated its guidelines to address using AI in employment decisions, stating that companies can be held liable for AI discrimination in hiring based on biased criteria.
Related Article: Information Source? No. But ChatGPT Can Help Employees in These Areas
Create Guardrails and Upskill Your People to Ensure Ethical Use of Generative AI
Develop ethical guidelines and policies around the use of generative AI in your business now to ensure equity remains at the forefront of its deployment into your organization.
You will need adaptable policies to keep up with AI advancements, ethical implications around new uses, and changing laws and regulations. Interestingly human beings can help build those guardrails using their critical thinking, empathy, adaptability, and inclusive leadership skills to review and determine the impacts on people and communities when deciding to use — or not use — an AI model.
Research finds that empathy is a critical skill for the future of work — and has many business benefits. Empathy, which cannot yet be automated by machines, is central to good design and ethical technology. Empathy helps you more readily see the needs of, and impact on, different people and communities when it comes to using tools like generative AI. The good news is that everyone can develop better empathy skills with intentional training and practice.
Related Article: Are You Giving Employees Guidelines on Generative AI Use? You Should Be
Build Equitable Pathways to Reskilling Programs — Especially for Women, Marginalized Communities and People in Jobs Most Impacted by Generative AI
If used carefully, generative AI has the power to democratize access to high-paying jobs that have traditionally relied on a combination of advanced education and specialized technical skills — and in fields where women have historically been underrepresented. Women earn only about 20% of undergraduate degrees in computer science and are about a quarter of people working in computer occupations in the United States. AI text-to-code generators mean that people no longer need computer science degrees to build software, opening a new pathway for women to enter the computing field.
Although there’s a wide gender gap for women working in AI development, no gender gap has yet been established around using generative AI at work, even if some early indicators signal that men are already using AI in their jobs more often than women.
As jobs shift in response to AI, we must build equitable access to reskilling programs without perpetuating existing gaps that keep women out of high-paying jobs.
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Technology has the potential to build a more equitable world and to create better access to higher-wage jobs. It also has the potential to deepen, reinforce or entrench existing biases, making the world more isolated and less equal. To build a more equitable future of work, we must ensure that algorithms, AI and other emerging technologies are developed by diverse teams of people working in tandem with machines, and are monitored closely to keep bias from repeating its old patterns.
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