Artificial intelligence is gaining ground in both popularity and funding. VCs are increasingly investing in startups with AI solutions, with last year’s tally of $59 billion in funding more than doubling 2020’s total of $28 billion. The current state of AI sees the growth of startups specializing across a broad range of disciplines, from content creation to logistics, accounting, cybersecurity, and more.
This growth is self-evident of a rise toward ubiquity, but the industry can’t wholly succeed without focusing on ethical issues with AI systems. In the U.S., AI ethics and bias are top priorities for academic institutions. Research continually finds AI bias that favors certain demographics, regardless of steps taken to exclude identifying information, such as age, race, gender, and sexual orientation.
It holds true even in venture capital, where AI business solutions determine which startups and entrepreneurs get funded. Only $3 for every $100 is invested in women, which boils down to how the pitches are evaluated. Men are exponentially more likely to use aggressive adjectives that earn the AI’s favor and get them hired.
AI ethics and bias are a lot more complicated than initially thought, and it’s an ongoing effort that everyone should focus on. While pushing for automation, it’s important to ensure these automated systems are fair for everyone. Here’s what to know about bias in AI.
What Is the Current State of AI?
AI is seemingly involved in everything, with specialized machine learning algorithms programmed to accomplish workflows throughout the average business. As its capabilities expand at exponential rates, AI’s climax might be either the best or worst thing to happen to humanity; if we fail to control and contain its adverse effects, we might face unintended repercussions.
AI bias can occur at every stage, from development to limited data training and testing or even unexpected factors that arise from its real-world usage. It’s difficult to tell whether an AI is working correctly, especially if it’s giving the right output. This can make it seem as though it’s doing the right thing, but it can also be a red flag for data bias.
This can lead to organizations depending on AI that might not be behaving as expected. It’s especially true as AI evolves, and some systems are subject to problems such as catastrophic forgetting. This happens with deepfakes, as AI trained to recognize them gets tricked by newer deepfake technologies. Resolving bias in AI is necessary to keep these systems growing correctly, and it takes four key areas to accomplish:
1. Technical safety
As useful as AI can be, it’s important to maintain security and data integrity. Bad actors constantly seek to exploit technologies, and AI is no different. In fact, hackers are leveraging AI to bypass traditional cybersecurity measures. Every AI deployment should feature the highest grade of security, including (but not limited to) two-factor sign-ins, security models, and robust DevOps.
2. Human oversight
In the U.S., AI ethics and bias regulations are still catching up, but the EU’s Artificial Intelligence Act provides a base for understanding how to prevent AI bias. Each deployment has unique risks, depending on the detrimental effects it can have on people’s lives. Humans must identify these risks and remain involved in training the algorithms. Whether we’re in charge or simply in the loop, humans will remain essential to ethical AI development.
3. Data governance
Like any technology, data privacy and security are essentials. AI systems require agile data governance mechanisms that can effectively analyze results and provide recommendations. This data fabric should be compliant with all data regulations, including GDPR, CCPA, ISO, and more. Not skipping this step is essential, as noncompliance can lead to fines, lawsuits, or worse.
4. Diversity, equity, and inclusion
As illustrated above, DEI is already infused into most corporate policies, but that isn’t enough. AI systems can only work with the data available to them, and biased data leads to human bias being amplified by automation. AI systems have to be accessible to everyone, regardless of age, race, gender, sexual orientation, disability, or affiliation. Using a robust data set is the only way to avoid IRL consequences for real people.
Artificial intelligence is one of the most critical technologies growing in use today. Its deployment enables optimized workflows, more efficient employees, and access to a higher level of business performance. However, achieving this isn’t automatic because AI requires training and configuration to ensure it accounts for every possibility, not just every possibility of which a small development team is aware.
Image by Ryan Stone