Mastercard Uses AI to Shut Down Compromised Cards in Real Time
Introduction
Artificial intelligence (AI) has tremendous potential to transform our world for the better. However, the current state of AI also presents significant risks if development continues without diverse perspectives. Many recent examples demonstrate how bias can become baked into AI systems, leading to harmful real-world outcomes. This underscores the urgent need for inclusion and diversity in shaping the future of AI.
If current demographic trends in the tech sector persist, we face the prospect of a small, homogenous group having outsized influence over one of the most transformative technologies in human history. This should give us pause. While brilliant technologists may create wonders, they operate within cultural blind spots. Unaddressed biases can become embedded within systems, reflecting unfair social hierarchies instead of elevating human potential.
True innovation requires diversity of thought and experience. As AI becomes further integrated into business, government, and daily life, we cannot afford to leave anyone out of the conversation. The best way to maximize the benefits of AI while mitigating risks is to consciously build inclusion into the design process. This will require proactive efforts to increase diversity within AI teams, establish guidelines to avoid bias, and involve impacted communities through co-design. The future demands that we rise to meet this challenge.
The Diversity Crisis in AI
The technology industry, especially artificial intelligence, suffers from a major lack of diversity. Women, minorities, and other underrepresented groups make up small fractions of the workforce in top technology companies and AI research labs. For example, only about 25% of AI researchers are women. The field is dominated by white and Asian men.
This homogeneous workforce leads to AI systems reflecting the same biases present in society. The people building these systems naturally bring their own perspectives and blindspots. Without diverse voices in the room, certain issues around fairness, accountability, and inclusiveness go overlooked.
The lack of diversity starts from the education pipeline. There are not enough students from underrepresented backgrounds pursuing studies in science, technology, engineering and math (STEM). Even for those who enter the tech industry, unfair biases and unwelcoming cultures cause higher attrition rates among women and minorities.
Overall, the current state of diversity in AI research and development is unsatisfactory. Having inclusive teams and perspectives is crucial for building unbiased systems that work equally well for all people. The industry needs major interventions to bring more women, minorities and people from various socioeconomic backgrounds into the fold.
How Bias Enters AI Systems
AI systems reflect the data used to train them as well as the biases of their creators. If the training data contains historical biases, the AI will replicate and amplify those biases. For example, facial recognition systems trained on datasets that underrepresent people of color will have higher error rates for those groups.
Algorithms can also introduce bias even when training data is balanced. The choices made in coding algorithms - what data to focus on, what correlations to look for, how different factors are weighted - inject the subjective perspectives of the programmers. These choices are often not transparent and their impacts not fully considered. So algorithms can discriminate in ways that were not intended but are difficult to detect.
Overall, biased data combined with biased algorithms leads to discriminatory and unfair AI systems. The key to creating ethical, inclusive AI is ensuring diverse perspectives are involved at all stages of development - from data collection through training, testing, and deployment. With inclusive teams and practices, harmful biases can be avoided from the start.
Real-World Consequences of Biased AI
AI systems are increasingly being used to make important decisions that impact people's lives, from approving loan applications to predicting recidivism rates in the criminal justice system. Unfortunately, biases in the data and algorithms used to train these AI systems can lead to discriminatory and harmful outcomes.
For example, facial recognition algorithms have been shown to have higher error rates for women and people of color due to biases in the training data. This can lead to false matches and wrongful arrests. In 2018, the ACLU found that Amazon's facial recognition tool incorrectly matched 28 members of Congress with mugshots in a database. The false matches disproportionately affected people of color.
Biased algorithms are also used to determine access to healthcare, housing, and employment opportunities. An algorithm used by US hospitals to allocate healthcare to patients was found to racially discriminate, providing lower levels of care to Black patients than white patients. In hiring, algorithms trained on biased data can filter out women and minority candidates.
These examples demonstrate how real harm can occur when biased AI is deployed in high-stakes decisions. Without thoughtful evaluation of these systems, marginalized groups bear the brunt of discriminatory and unethical AI. More inclusive and ethical approaches to AI development are urgently needed.
The Business Case for Inclusive AI
Companies have compelling reasons to prioritize diversity and inclusion when developing AI systems. Having diverse teams and voices involved leads to better outcomes that benefit the company's bottom line. Here are some of the key business benefits of inclusive AI practices:
Broader understanding of users and markets - Teams with diversity of gender, race, age, and background will have greater insight into the needs of different customer segments. Inclusive teams can help ensure products work well for diverse users.
Increased innovation - Research shows that diverse teams are more innovative. Bringing together people with different perspectives and experiences sparks creativity. This innovation gives companies a competitive edge.
Avoiding PR crises - Lack of diversity increases the risk of avoidable mistakes, like biased algorithms or offensive marketing campaigns. Inclusive teams help avoid these PR crises that damage brand reputation.
Access to wider talent pools - Companies that champion diversity and inclusion are more attractive to top talent from underrepresented groups. This widens the talent pipeline.
Improved productivity - When employees feel included, they are more engaged, motivated, and productive. Homogenous "groupthink" can stifle productivity.
In summary, prioritizing inclusive AI practices makes good business sense. Companies that embrace diversity of thought, backgrounds, and insights in their AI development will be poised for success. They'll build better products, attract top talent, avoid missteps, and foster innovation.
Increasing Diversity in AI Teams
Diversifying AI teams requires intentional strategies around recruiting and retention. Many tech companies rely on traditional channels like employee referrals and elite universities to find talent. However, this approach tends to replicate the existing lack of diversity.
Here are some strategies to build more inclusive AI teams:
Expand recruiting efforts beyond top CS programs to include historically black colleges and universities (HBCUs), women's colleges, bootcamps, and community colleges. Actively recruit from underrepresented groups.
Remove biased language and requirements from job postings. Focus on skills rather than degrees or years of experience, which can disadvantage certain groups.
Partner with organizations that support women, people of color, veterans, LGBTQ+ people, and people with disabilities in tech. Attend their events and conferences to meet candidates.
Offer paid internships, apprenticeships, training programs and scholarships to people from underrepresented backgrounds trying to enter the field.
Interview candidates using structured rubrics and diverse panels to minimize bias. Train interviewers on inclusive practices.
Make diversity, equity and inclusion a core part of your workplace culture. Foster a sense of belonging through mentorship, affinity groups, and celebrating multiculturalism.
Offer competitive compensation, flexible work arrangements, family leave, and growth opportunities to retain diverse talent.
Making the effort to build inclusive AI teams allows for a greater diversity of perspectives to inform the development of fair, ethical and innovative AI systems.
Co-Designing AI Systems
To create more inclusive AI systems, organizations need to partner directly with groups affected by the technology. This process of co-design brings in diverse perspectives early and often.
Key steps for co-design include:
Assembling a diverse advisory group: This should include people from marginalized groups who can speak to how the AI system will impact them. Their lived experiences are invaluable.
Running participatory design workshops: These interactive sessions allow for brainstorming, prototyping, and feedback. The goal is to incorporate diverse users' needs into the system.
Doing field studies: Immersing designers in affected communities helps build empathy and uncover overlooked issues. Seeing users interact with an AI system in real-world settings is enlightening.
Creating user panels: Representative panels can test AI systems throughout development and identify any biases or harms. This allows for ongoing refinement and improvement.
Fostering two-way communication: There must be open channels for underserved groups to raise concerns and give input on the AI system pre- and post-launch. This feedback loop enables continuous evolution.
By deeply engaging impacted communities, organizations can steer AI in a more just direction. Co-design also builds public trust and buy-in for AI systems that serve everyone equitably.
Testing for Fairness
Ensuring AI systems are fair and unbiased requires rigorous testing methodologies. Here are some techniques for evaluating bias in AI systems:
Algorithmic auditing - Examining how an AI system makes decisions by testing it with a range of inputs. This reveals whether certain groups receive consistently less favorable outcomes.
A/B testing - Running A/B tests with different user groups allows comparing how an AI system performs for each demographic segment. Significant performance gaps indicate bias.
Simulated datasets - Testing a system on synthetically generated datasets with different combinations of gender, age, ethnicity etc. exposes bias towards certain attributes.
Crowdsourced evaluation - Having a demographically diverse group of people evaluate an AI system's outputs highlights areas where biases persist. Their feedback improves fairness.
Explaining outputs - Requiring AI systems to explain their reasoning helps identify cases where improper attributes like race or gender are influencing decisions.
Bias bounties - Crowdsourcing bias discovery by allowing people to probe systems and receive rewards for uncovering unfairness, similar to bug bounties in software.
Regular bias testing builds accountability and trust in AI. Prioritizing inclusion and diversity in the development process further enhances fairness. Overall, a multilayered approach is required to ensure AI does not perpetuate real-world inequality.
Establishing Ethical Guidelines
Companies building AI systems have a responsibility to establish ethical guidelines to prevent bias and promote fairness. This starts with developing clear company values and policies around diversity, equity and inclusion. Leadership should make a commitment to hiring diverse teams and creating an inclusive work culture.
Companies should also create internal policies and training programs to educate all employees on avoiding bias in AI systems. They can adopt ethical frameworks like the Asilomar AI Principles or IEEE Ethically Aligned Design standards. These provide guidance on issues like transparency, accountability, privacy, avoiding unfair bias, and more.
It's critical that companies implement rigorous testing processes to detect bias and correct issues prior to product launch. However, guidelines and testing alone are insufficient. Companies must also empower diverse internal teams and external advisory boards to provide ongoing critical perspectives on potential harms. They should establish accessible mechanisms for reporting issues or complaints around biased outcomes.
By establishing strong ethical foundations across policies, practices and culture, companies can drive innovation in AI while ensuring socially responsible outcomes. The path forward relies on a shared commitment to inclusion, diversity and human values.
The Future of Inclusive AI
As AI becomes more ubiquitous in our daily lives, ensuring it reflects the diversity of human experiences and perspectives will only grow in importance. Though the AI community still has work to do in addressing inclusion, the momentum is shifting in a positive direction.
Many major technology companies have established ethical AI departments and review boards to provide oversight and establish best practices. Governments are also beginning to establish regulations around transparency and eliminating bias in AI systems. Moving forward, it will be critical that these efforts involve diverse voices in meaningful ways.
We all have a role to play in advocating for inclusive AI. This includes calling on technology companies and governments to make diversity and inclusion core priorities. It also means supporting organizations that are taking concrete steps to address these issues.
At an individual level, those working in AI should continuously educate themselves on how bias manifests and take proactive steps to mitigate it. Teams should actively seek diversity in hiring and provide training around creating ethical, inclusive products.
The potential for AI to drive tremendous progress is real. But realizing an equitable future requires centering the voices missing from the conversation today. By embracing the power of inclusion, we can develop AI that reflects the diversity of our shared humanity. The responsibility lies with all of us.
