Like other forms of AI, generative AI can influence a number of ethical issues and risks surrounding data privacy, security, policies and workforces. Generative AI technology can also potentially produce a series of new business risks like misinformation, plagiarism, copyright infringements and harmful content. Lack of transparency and the potential for worker displacement are additional issues that enterprises may need to address.
“Many of the risks posed by generative AI … are enhanced and more concerning than others,” said Tad Roselund, managing director and senior partner at consultancy BCG. Those risks require a comprehensive approach, including a clearly defined strategy, good governance and a commitment to responsible AI. A corporate culture that embraces generative AI ethics must consider eight important issues.
1. Distribution of harmful content
AI systems can create content automatically based on text prompts by humans. “These systems can generate enormous productivity improvements, but they can also be used for harm, either intentional or unintentional,” explained Bret Greenstein, partner, cloud and digital analytics insights, at professional services consultancy PwC. An AI-generated email sent on behalf of the company, for example, could inadvertently contain offensive language or issue harmful guidance to employees. Generative AI should be used to augment, not replace humans or processes, Greenstein advised, to ensure content meets the company’s ethical expectations and supports its brand values.
2. Copyright and legal exposure
Popular generative AI tools are trained on massive image and text databases from multiple sources, including the internet. When these tools create images or generate lines of code, the data’s source could be unknown, which can be problematic for a bank handling financial transactions or pharmaceutical company relying on a formula for a complex molecule in a drug. Reputational and financial risks could also be massive if one company’s product is based on another company’s intellectual property. “Companies must look to validate outputs from the models,” Roselund advised, “until legal precedents provide clarity around IP and copyright challenges.”
3. Data privacy violations
Generative AI large language models (LLMs) are trained on data sets that sometimes include personally identifiable information (PII) about individuals. This data can sometimes be elicited with a simple text prompt, noted Abhishek Gupta, founder and principal researcher at the Montreal AI Ethics Institute. And compared to traditional search engines, it can be more difficult for a consumer to locate and request removal of the information. Companies that build or fine-tune LLMs must ensure that PII isn’t embedded in the language models and that it’s easy to remove PII from these models in compliance with privacy laws.
4. Sensitive information disclosure
Generative AI is democratizing AI capabilities and making them more accessible. This combination of democratization and accessibility, Roselund said, could potentially lead to a medical researcher inadvertently disclosing sensitive patient information or a consumer brand unwittingly exposing its product strategy to a third party. The consequences of unintended incidents like these could irrevocably breach patient or customer trust and carry legal ramifications. Roselund recommended that companies institute clear guidelines, governance and effective communication from the top down, emphasizing shared responsibility for safeguarding sensitive information, protected data and IP.
5. Amplification of existing bias
Generative AI can potentially amplify existing biases — for example, bias can be found in data used for training LLMs outside the control of companies that use these language models for specific applications. It’s important for companies working on AI to have diverse leaders and subject matter experts to help identify unconscious bias in data and models, Greenstein said.
6. Workforce roles and morale
AI can do a lot more of the daily tasks that knowledge workers do, including writing, coding, content creation, summarization and analysis, said Greenstein. Although worker displacement and replacement have been ongoing since the first AI and automation tools were deployed, the pace has accelerated as a result of the innovations in generative AI technologies. “The future of work itself is changing,” Greenstein added, “and the most ethical companies are investing in this [change].”
Ethical responses have included investments in preparing certain parts of the workforce for the new roles created by generative AI applications. Businesses, for example, will need to help employees develop generative AI skills such as prompt engineering. “The truly existential ethical challenge for adoption of generative AI is its impact on organizational design, work and ultimately on individual workers,” said Nick Kramer, vice president of applied solutions at consultancy SSA & Company. “This will not only minimize the negative impacts, but it will also prepare the companies for growth.”
7. Data provenance
Generative AI systems consume tremendous volumes of data that could be inadequately governed, of questionable origin, used without consent or contain bias. Additional levels of inaccuracy can be amplified by social influencers or the AI systems themselves.
“The accuracy of a generative AI system depends on the corpus of data it uses and its provenance,” explained Scott Zoldi, chief analytics officer at credit scoring services company FICO. “ChatGPT-4 is mining the internet for data, and a lot of it is truly garbage, presenting a basic accuracy problem on answers to questions to which we don’t know the answer.” FICO, according to Zoldi, has been using generative AI for more than a decade to simulate edge cases in training fraud detection algorithms. The generated data is always labeled as synthetic data so Zoldi’s team knows where the data is allowed to be used. “We treat it as walled-off data for the purposes of test and simulation only,” he said. “Synthetic data produced by generative AI does not inform the model going forward in the future. We contain this generative asset and do not allow it ‘out in the wild.'”
8. Lack of explainability and interpretability
Many generative AI systems group facts together probabilistically, going back to the way AI has learned to associate data elements with one another, Zoldi explained. But these details aren’t always revealed when using applications like ChatGPT. Consequently, data trustworthiness is called into question.
When interrogating generative AI, analysts expect to arrive at a causal explanation for outcomes. But machine learning models and generative AI search for correlations, not causality. “That’s where we humans need to insist on model interpretability — the reason why the model gave the answer it did,” Zoldi said. “And truly understand if an answer is a plausible explanation versus taking the outcome at face value.”
Until that level of trustworthiness can be achieved, generative AI systems should not be relied upon to provide answers that could significantly affect lives and livelihoods.