While the uses of AI tools can seem unlimited, it’s critical that their expertise does not go unquestioned; AI tools are only as reliable as the data they’re trained on — and the people who build them.
Issues related to privacy, biases, and transparency remain paramount for building AI systems that are both ethical and accurate. As corporations continue to embed AI into their day-to-day processes, establishing frameworks ensuring AI applications are within legal and ethical bounds is increasingly important.
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The Importance of AI Ethics
Understanding the ethical implications of AI is critical for leaders:
First, AI ethical literacy gives leaders an understanding of the potential issues AI could cause, allowing them to protect their companies from lawsuits and reputational damage.
Second, understanding AI ethics helps leaders build a holistic picture of the coming AI-Age, and the concomitant risks and opportunities.
AI ethics examines the societal implications of widespread AI usage around issues like fairness and privacy. It also explores how AI affects the environment and its potential impact on the workforce: AI data centers require more water resources than traditional data centers, and even AI innovators like Anthropic CEO Dario Amodei foresee widespread white-collar job loss, projecting that AI could replace 50 percent of all entry-level white-collar jobs within the next five years.
Understanding how ethical issues affect a business’ day-to-day operations, such as privacy-related issues, in addition to the broader implications of AI on the economy, the workforce, and the environment, will enable leaders to make informed and balanced decisions.
Ethical Challenges in AI
Leaders are facing a host of challenges when it comes to managing AI data and privacy, biases, transparency issues, and more.
Michael Impink, instructor of AI Ethics in Business at Harvard DCE’s Professional and Executive Development division, weighs in on how executives and business leaders can meet these challenges head-on.
“For leaders, awareness is the number one step,” Impink says. Once leaders know where ethical AI issues might exist, they can begin to generate solutions.”
But because AI is moving so quickly, there’s no clearly defined step-by-step process for resolving all ethical issues.
“AI is idiosyncratic to what you want it to do, so there’s no one-size-fits-all approach,” he adds.
Understanding the main ethical challenges AI presents — and creatively generating solutions — is mandatory for the leaders of tomorrow.
AI Data & Privacy
Data privacy is paramount for most companies. Ensuring their customers’, patients’, or business’ proprietary information remains secure is mission-critical.
But AI creates other avenues for bad actors to gain access to a company’s sensitive information, potentially exposing the business to litigation. For example, businesses that collect Personal Identifying Information (PII) have a legal responsibility to keep that information secure. If PII is given to an AI tool, the tool needs to be secure from both external cyberattacks and any internal manipulation. It’s critical that leaders understand the risks posed by AI malware and develop internal cybersecurity systems to detect and mitigate AI threats.
Possible Bias in AI
Biases are one of the biggest ethical challenges for AI systems, but most business leaders miss the implications of biases on business outcomes.
Just like working with an inaccurate business model, a biased AI tool can lead to a host of poor outcomes: inaccurate predictions, litigation (if the biases are found to negatively impact a protected class, for example), and wrongheaded conclusions.
According to Impink, there are three main sources for biases in AI: The programmers, the algorithm, or the training data.
“The programmers creating the AI could be biased,” Impink says. “The algorithm could be biased, it could be overweighting something, or the bias could be something inherent in the algorithm. The training data itself could be biased, or these learning materials could be limiting some important information.”
AI Modeling Transparency and Explainability
In an era where AI systems increasingly shape business decisions in sectors like finance, healthcare, and education, humans need to understand the algorithmic priorities and rationales that drive AI decision-making. If an AI model denies a loan, flags a tumor, or prioritizes a job applicant, humans must be able to trace the reasoning behind that decision. An ethical application of AI tools necessitates deep human understanding to ensure that decisions are made fairly.
“If it becomes commonplace to use AI, the firms who use it ethically and responsibly will gain a competitive advantage,” Impink says. “The ones who don’t might have a harder time winning contracts or accessing data.”
Without a clear window into how AI arrives at its conclusions, businesses run the risk of creating so-called “black-box” systems where the algorithms automating decisions are inscrutable to employees managing the systems.
When decisions become biased or limit opportunities for recourse, current inequalities are likely to be reinforced and public trust in businesses or AI tools could degrade rapidly. If people begin to feel that AI algorithms are making life-altering decisions in ways even experts can’t explain, they’re likely to lose trust in those institutions and tools.
If it becomes commonplace to use AI, the firms who use it ethically and responsibly will gain a competitive advantage.
Michael Impink
The Impact of AI on Employment
The rise of AI in the workplace presents a complex ethical challenge for the global job market. On one hand, AI promises to automate repetitive tasks, boost productivity, and unlock entirely new industries. On the other, the speed of AI adoption threatens white-collar jobs across industries and roles, from software engineers to copywriters, sales reps to HR associates.
Historically, big waves of technological change have led to increased economic productivity. Proponents of AI argue that these periods freed workers from low-wage, rote work and delivered them to higher-value, more interesting work — and the AI revolution will do the same.
However, it’s unclear what new roles will be created, and what new training or upskilling opportunities will be available to prepare workers for new positions. As the speed of AI development and adoption increases, there’s a risk that development will outpace retraining.
Governments may need to step in to support this major transition within the workforce through a provision for Universal Basic Income (UBI).
“We might see people working 30 hours a week and receiving some form of UBI. But we’re not there yet — we haven’t seen the productivity gains,” Impink says.
But widespread AI use could also create new jobs. AI-experts in different fields will likely be in high-demand in the coming years.
AI Governance
As AI technology becomes more widespread, international bodies are recognizing the need for global coordination to address challenges and risks while also distributing and maximizing benefits. For example, the Organization for Economic Co-operation and Development (OECD) issued its OECD AI Principles, designed to promote an innovative yet trustworthy use of AI that respects democratic norms.
Similarly, the United Nations Secretary-General has established a board of 39 experts from various disciplines to act as a High-Level Advisory Body on AI. By engaging stakeholders that include governments, the private sector, and society at-large, the board will recommend strategies for international AI governance that respects human rights and helps meet sustainable development goals.
Today’s Regulatory Landscape for AI
The AI regulatory landscape is rapidly evolving in the United States. The Trump Administration’s sweeping tax and spending package includes an unusual and hotly debated proviso — no state AI regulations for 10 years. In the most recent version of the bill, states that pass regulations on AI models and systems wouldn’t be able to access the $500 million in federal funds earmarked for AI infrastructure and deployment.
Further afield, the European Union passed the EU AI Act, presenting a comprehensive framework for classifying AI tools based on the risks they present to users. Regulations are applied based on risk level, with AI applications deemed high risk subject to greater scrutiny and more regulations. High risk AI applications include those that impact safety (such as AI used in aviation or medical devices) or fundamental rights (such as AI used in law enforcement, educational training, and more).
While AI regulation in the US is up for debate, US firms with a global footprint using AI may still find themselves facing regulations:
“The E.U. regulates the developed world,” Impink says. “U.S. firms adhere to E.U. regulations if they want to work internationally, so everyone really follows Europe.”
The Future of AI Ethics
To some, AI seems like a fad. Free, public-facing LLMs like ChatGPT and Claude regularly make mistakes and aren’t up-to-date on the latest news and trends, leading some to underestimate the power of this technology.
However, internal AI systems are more powerful than publicly available LLMs and can become ever-more refined through specific prompting. As development continues — to the point where AI systems are developing themselves and other systems — reliability concerns will drop. However, more sophisticated AI systems raise other concerns, like the possibility of superintelligence and what that could mean for the workforce and society.
Is Superintelligence A Possibility?
A.I. superintelligence — an AI system whose intelligence surpasses that of humans — is a hotly debated subject. Some experts believe we’ll see superintelligence by the end of the decade, and others say that it will probably never happen. But what exactly is AI superintelligence?
AI superintelligence refers to systems that exceed human intelligence and can autonomously learn and innovate beyond their initial programming.
“Personally, I don’t think it’s possible,” Impink says. Because A.I. tools are trained on human materials and iterated upon by human prompting, “there’s some level of creativity it won’t be able to reach.”
Ready to dive deeper into AI?
As AI transforms the workforce, leaders need tailored training to stay up-to-date. Harvard’s Department of Continuing Education offers several courses related to artificial intelligence to support business leaders as they use AI to drive business success.
As businesses implement AI into their workflows, systems, and teams, establishing ethical processes around data privacy, fairness, and transparency is critical for success. Companies that pursue an ethical approach to AI are likely to avoid litigation and preserve their brand reputation.
