By Simon Blanchette, McGill University
As artificial intelligence (AI) continues to reshape industries and transform workplaces, it’s imperative that organizations and leaders examine not only its impact on productivity, innovation and economic gains, but also the ethical implications tied to these transformative technologies.
Integrating an equity, diversity and inclusion (EDI) lens into AI systems is no longer a luxury or optional. It’s essential to ensure AI benefits everyone, including equity-deserving groups such as women, Indigenous Peoples, people living with disabilities, Black and racialized people, and 2SLGBTQ+ communities.
Without this commitment, AI risks reinforcing the existing biases and inequalities, including those based on gender, race, sexual orientation, and visible and invisible disabilities. We already know the deep impact of AI on human resources and recruitment, but its impacts go beyond that.
While AI adoption gaps often dominate the conversation, equally critical are the ethical concerns surrounding its development and deployment. These issues have profound implications for leadership, trust and accountability. Leaders and organizations need greater supports, education and guidance to responsibly guide AI’s integration into the workplace.
The need for ethical AI
AI has the potential to shed light on and address systemic discrimination, but only if it’s designed and used ethically and inclusively. Machine learning algorithms learn patterns from large datasets, but these datasets often reflect existing biases and underrepresentation.
AI systems can inadvertently reinforce these biases. As a scholar and practitioner, I know that data is not neutral; it is shaped by the context — and the people — involved in its collection and analysis.
A clear example of this risk is Microsoft’s Tay Twitter chatbot, which began re-posting racist tweets and was shut down only 16 hours after its release. Tay was “learning” from its interactions with Twitter users.
Such incidents are not only damaging from a public relations angle, they can also affect employees, particularly those from marginalized communities, who may feel alienated or unsupported by their own organization’s technology.
Similarly, the AI avatar app Lensa was shown to turn men into astronauts and other fun and empowering options, while sexualizing women. In industries already grappling with sexism, such as gaming, this sends a troubling message to users, reinforces stereotypes and creates a hostile work environment for employees.
AI technology creators and users must incorporate EDI principles from the ground up. Diversity in AI development teams is one of the most effective safeguards, as it minimizes blind spots.
By embedding EDI values into AI from the outset, creators and users can ensure AI tools and their usage are not compounding the barriers faced by equity-deserving groups, and that corrective measures are developed to mitigate existing and emerging issues.
Leaders must lead
Leaders must recognize how AI can drive changes. It can uncover hidden biases and disparities which can force an uncomfortable reckoning and require humility. Recognizing bias can be challenging — no one wants to be biased, yet everyone is.
By integrating EDI and AI, leaders can open new opportunities for equity-deserving groups. For instance, by combining the power of AI and diverse teams, we can foster inclusive product design that will cater to more consumers, and lead to more success for the organization.
AI should be viewed as an additional tool to help decision-makers, not as a substitute for it. Leaders must ensure AI systems are designed and deployed with inclusivity at their core. They need to address potential disparities before they are encoded into algorithmic decision-making, and correct remaining errors down the line.
Accountability remains at the human level; leaders need humility and courage.
AI is here to stay
Transparency, accountability and inclusivity are becoming increasingly essential in a world where consumers and employees alike are demanding more ethical practices from companies and workplaces.
Organizations that embed ethical AI principles into their systems will not only avoid reinforcing inequalities, but also position themselves as leaders in the market. Some of these principles include fairness, transparency, human oversight, diversity in learning/datasets representativeness and non-discrimination.
Addressing this can build trust, bridge gaps in adoption and counter biases that can perpetuate inequality. AI is here to stay and it’s bound to play an even bigger role in all our lives over the next few years. As it becomes increasingly integral to society, implementing these kinds of principles is essential.
Clear accountability mechanisms and practices can help ensure AI systems operate in a way that aligns with the values of an organization and society at large. These considerations include verifying and validating AI outputs, ensuring explicability (the ability to explain and justify results), and designing and implementing mechanisms to correct and address biases.
Leaders must foster a culture of innovation and responsibility, where developers, data scientists and other stakeholders understand their roles in minimizing biases, ensuring fairness and prioritizing inclusivity. This can include pursuing EDI certification to build awareness and accountability around biases at all levels of an organization.
Without these commitments, public trust in AI may be undermined and erode the potential benefits that these technologies offer — something that has already been an issue over the last few years.
Strategies for dealing with AI
Leaders have a crucial role to play, recognizing that AI, while transformative, is not a replacement for human oversight. AI on its own is not a panacea to remove biases. To move forward, organizations should:
- Involve diverse teams in AI development to ensure varied perspectives and lived experiences shape the technology, improve data and frame its usage.
- Cultivate inclusive workplaces where members from equity-deserving groups feel safe to be authentic and to use their voice and feel heard and valued, including when they point out shortcomings and biases.
- Prioritize upskilling and reskilling of employees and leaders to improve AI literacy and strengthen critical transferable skills like critical thinking, adaptability, creativity and EDI-related skills.
- Establish clear accountability frameworks and conduct regular, rigorous audits to detect and mitigate bias in AI systems. Frameworks should evolve as AI evolves.
- Work with other external groups, including governments, non-profits or educational institutions — like the Institute of Corporate Directors, the Vector Institute for Artificial Intelligence or Mila AI Institute — to create an ecosystem where supports, resources and information are readily available.
By prioritizing these practices, organizations and leaders alike can lead AI to be both a force for innovation and economic growth, and a model for ethical responsibility furthering the inclusion of equity-deserving groups. AI should benefit everyone in the workplace and in society.
Simon Blanchette, Lecturer, Desautels Faculty of Management, McGill University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
