We've all heard the horror stories: biased algorithms, privacy breaches, AI gone rogue. But does that mean we should shy away from artificial intelligence? Hardly. It means we need to get smarter about how we use it. Including robust AI governance protocols and a strong adherence to ethical principles.
Beyond the scope of your organizational goals for AI implementation, there are bigger, societal issues with artificial intelligence and its implications. The World Economic Forum lists the following 9 ethical concerns. Some of these are already upon us, while others sound like science fiction:
Of the above 9 ethical concerns, it’s hard to solve unemployment, social inequality, the singularity, and the rights of AIs on an organizational level. But you can certainly safeguard against security breaches, bias, and stupidity with the right protocols in place.
Bringing it down from a society-wide perspective, let’s look at the most crucial ethical dilemmas and potential risks that you can address on an organizational level.
When implementing artificial intelligence of any sort in your organization, you want to stay connected to basic human values and ethical principles. Here are some guidelines you can use as a framework:
If you follow these principles, you’ll buffer yourself against most ethical risks and pitfalls.
There are a wide range of ethical scenarios that crop up for artificial intelligence applications. In each of the following examples, human intelligence and human values counteracted the potentially unethical fallout of AI.
A hospital's AI-powered diagnostic tool showed bias against certain ethnic groups, leading to potential misdiagnoses.
The hospital partnered with diverse medical institutions to retrain the AI on a more representative dataset, implemented ongoing bias checks, and added a human oversight layer for high-stakes diagnoses.
A bank's AI-driven loan approval system was rejecting applications from qualified individuals in low-income neighborhoods.
The bank revised its AI model to consider alternative credit data, implemented fairness metrics, and introduced a human appeal process for rejected applications.
An e-commerce giant's AI-powered pricing algorithm was found to be engaging in unintentional price discrimination based on user location.
The company redesigned its algorithm to focus on supply-demand dynamics rather than user data, and implemented regular audits to ensure fair pricing across all demographics.
An automotive company's AI-powered worker productivity monitoring system was found to be infringing on employee privacy and causing undue stress, leading to health issues and high turnover rates.
The company redesigned the system to focus on overall production efficiency rather than individual tracking, implemented clear data usage policies, allowed workers to access their own data, and introduced AI-assisted ergonomic improvements to enhance worker well-being alongside productivity.
A university's AI admissions screening tool was found to favor applicants from certain high schools, potentially perpetuating educational inequality.
The university removed school-specific data from the AI's decision-making process, introduced a holistic review system combining AI recommendations with human judgment, and increased outreach to underrepresented schools.
A large corporation's AI-driven resume screening tool was inadvertently filtering out qualified female candidates for technical positions.
The company retrained the AI using gender-neutral language, implemented regular bias audits, and introduced a diverse panel of human recruiters to review AI recommendations.
A law firm's AI-powered case prediction tool was showing bias towards outcomes favoring larger corporate clients.
The firm recalibrated the AI using a more balanced case history, introduced confidence intervals for predictions, and established a policy of using the AI tool as a supplement to, not a replacement for, legal expertise.
An agtech company's AI-driven pesticide application system was optimizing for maximum crop yield, leading to overuse of chemicals harmful to local ecosystems and potentially affecting food safety.
The company recalibrated its AI to balance yield with environmental impact and food safety standards. They incorporated data from environmental sensors and long-term ecological studies, implemented strict upper limits on chemical use, and introduced transparency features allowing consumers to trace the AI-guided growing practices for their food.
A streaming service's artificial intelligence content recommendation system was creating "filter bubbles," limiting users' exposure to diverse content.
The company revised its algorithm to include "discovery" parameters, introduced user-controlled diversity settings, and began providing transparent explanations for its recommendations.
A city's AI-driven traffic management system was optimizing flow in wealthy areas at the expense of poorer neighborhoods.
The city government recalibrated the system to prioritize overall transit time reduction, introduced equity audits, and created a citizen oversight committee to ensure fair implementation across all areas.
When leaders consistently emphasize the importance of ethical AI practices and demonstrate their commitment through actions, it sends a clear message throughout the organization that ethics isn't just a buzzword, but a fundamental aspect of how your company operates.
Leaders need to align AI ethical oversight with the company's core values. This alignment creates a coherent framework that guides all AI-related activities, from development to deployment. It's not enough to have a separate set of AI ethics guidelines; they should be an extension of what the company already stands for.
Of course, fostering an ethical AI culture requires more than just words. Leaders must be willing to allocate real resources to these initiatives. This means dedicating budget, time, and personnel to develop and implement ethical AI practices. It could involve investing in training programs, developing auditing tools, or funding research into emerging ethical challenges.
Or, it could involve working with Talbot West on a set of AI governance protocols to keep you on the right side of ethical and legal issues as you integrate artificial intelligence into your enterprise. Contact us to learn how we can help.
As AI becomes more prevalent and powerful, we're seeing a rapidly evolving landscape that businesses need to navigate carefully.
Evolving regulations and compliance requirements are at the forefront. Governments and regulatory bodies worldwide are scrambling to catch up with the rapid advancements in AI technology. Whether you think their actions are heavy-handed or too weak, we’re in for a deluge of regulation—though it won’t be evenly distributed. Some states and nations will act more strongly than others, creating a patchwork of compliance demands.
We're seeing proposals like the EU's AI Act, which aims to categorize AI systems based on their risk level and impose stricter regulations on high-risk applications. In the U.S., while there's no comprehensive federal AI regulation yet, we're seeing movement at the state level and in specific sectors like healthcare and finance. Companies will need to stay agile, constantly updating their compliance strategies as these regulations take shape.
As AI systems become more autonomous and capable of making complex decisions, we'll need to grapple with questions of accountability and control. The development of AI in sensitive areas like healthcare or criminal justice will require particularly careful ethical scrutiny.
Generative AI will continue to grow in capability, and we’re already seeing the rise of agentic AI: artificial intelligence systems that can make decisions and take actions. As AI becomes increasingly agentic, expect ethical and moral principles to come ever more into sharp focus.
Consumers and employees alike are becoming more aware and concerned about the ethical implications of AI. We've seen instances where companies faced significant backlash due to perceived unethical use of AI, whether from biased hiring algorithms or privacy-invading data practices.
On the flip side, companies that demonstrate a strong commitment to ethical AI can build trust and loyalty among their stakeholders. As AI becomes more ubiquitous, a company's approach to AI ethics may become as important to its brand as its environmental or labor practices.
Looking ahead, we can expect AI ethics to become an increasingly central part of corporate strategy and governance. Companies might need to create dedicated AI ethics boards or appoint chief ethics officers to navigate these complex issues.
We also expect a growing demand for AI auditing and certification processes, similar to how we see financial or environmental audits today.
We’re excited to watch artificial intelligence technology unfold at such a rapid pace, and we’re also aware of the risks and potential impacts it can have.
We believe in embracing the potential of AI while upholding societal values and universal ethical principles. If you’d like our help navigating these thorny issues, we’d love to talk.
Here are three examples in which AI seemed to be operating in a manner not consistent with ethical and moral principles:
Here are the five commonly-cited pillars of AI ethics:
Why are people so worked up about AI? It's not just about robots taking our jobs (though that's definitely on the list). It's about fairness, privacy, and who's in control when things go wrong. It's about whether AI will make our world more equal or just amplify the inequalities we already have.
Let's break down why AI is such a hot-button issue. Here are the main existential and ethical risks critics highlight with artificial intelligence:
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
