Ensuring Ethical AI: From Principles To Practice

Tuna Demiray
May 29, 2024

The generative AI revolution is unfolding right before the eyes of society, and its impact on business will be profound. As the potential for automation and machine intelligence grows, ethical issues relating to customer privacy, worker displacement, and more must be confronted head-on.

In the not-too-distant past, the idea of an artificial intelligence capable of generating art, crafting stories, or comprehending complex data was beyond the scope of human imagination.

But with the dawn of the Generative AI era, this technology is rapidly reevaluating people’s expectations of machine intelligence and the potential for creative and new analytical capabilities. It's an era that is redefining people’s ideas about what machines can do and their potential for creative and analytical endeavors mankind never would have thought possible.

First Things First: What is Generative AI?

  • Generative AI is a revolutionary development in the field of artificial intelligence. It enables machines to generate new content by building on existing information such as text, audio, video, and code.
  • Industry leaders like Gartner consider Generative AI as one of the most impactful and rapidly evolving technologies.
  • This technology promises to deliver unprecedented productivity gains across businesses.
  • Generative AI represents a significant shift in the AI landscape with the potential to exceed expectations beyond mankind’s wildest dreams.
  • The technology has the potential to contribute significantly to the future of automation.
  • Exciting and incredible possibilities await as Generative AI sets the stage for significant growth and progress around the world

What are the Ethical Concerns of Generative AI?

Generative AI is a game-changing technology for businesses, but it comes with a set of ethical issues that must be addressed. Among these issues are the potential impact on data privacy, security, and policies.

However, generative AI may also introduce new risks, such as the spread of misinformation, plagiarism, copyright violations, and harmful content that businesses must contend with.  

Additionally, enterprises adopting generative AI must be transparent and consider the potential for worker displacement to navigate the ethical challenges of this emerging technology successfully.

The risks triggered by generative AI are astounding according to Tad Roselund, managing director and senior partner at consultancy BCG:

- Some risks are more worrisome than others

- Organizations cannot afford to underestimate these risks

- An integrated approach is necessary, including a clearly defined strategy, robust governance, and a steadfast commitment to responsible AI

- Companies must establish a corporate culture that embraces generative AI ethics

- Addressing eight imperative issues is crucial

- Generative AI can deliver its full potential while prioritizing ethics and accountability

- It's important to embrace generative AI while maintaining ethics and responsibility at the forefront.

1. Circulation Of Dangerous Media

The rise of Generative AI is not without its challenges, one of which is the distribution of harmful content.

As Bret Greenstein, Partner of Cloud and Digital Analytics Insights at PwC, has pointed out, AI systems can generate content automatically based on text prompts by humans, offering considerable productivity gains but also carrying the risk of unintentional or malicious harm.

For instance, an AI-generated email sent on behalf of the company could contain offensive language or give harmful guidance to employees. To guard against such risks, Greenstein advises companies to use Generative AI to supplement, not replace, human judgment and processes.

This approach ensures that AI-generated content meets ethical standards and aligns with brand values

2. Violations Of Privacy Policies

Data privacy violations are a significant risk of Generative AI - sensitive personally identifiable information (PII) could be compromised.

Training data used for these systems may include sensitive information such as personally identifiable information (PII), which the US Department of Labor defines as any data that "directly identifies an individual, e.g., name, address, social security number or other identifying number or code, telephone number, and email address."

The potential violation of user privacy presents a critical threat, including the possibility of identity theft or misuse for discriminatory or manipulative purposes.

Adherence to strict data privacy guidelines is crucial for both the developers of pre-trained models and companies.

3. Copyright Risks And Legal Consequences

Generative AI tools that are currently popular are trained on huge databases that contain text and images from various sources including the internet.

The data's source may not be known when these tools generate lines of code or images, which poses a significant problem for pharmaceutical companies that depend on complex formulas for developing new drugs or banks that deal with financial transactions.

In case the products of one company are based on another company's intellectual property, there may be significant financial risks and reputational damage.

To avoid financial risks and reputational damage, companies must validate the outputs of Generative AI models and ensure they don't violate creations owned by others.

Legal clarity on IP and copyright challenges is necessary, and a proactive approach must be adopted to minimize risks.

4. Unauthorized Release Of Sensitive Data

As the democratization of AI capabilities gains momentum, it is imperative to acknowledge the risks arising from inadvertent disclosure of sensitive information, which further underlines the significance of diligent use of user data.

Regrettably, the lure and convenience of generative AI tools can sometimes overshadow the importance of data security. An employee's unintentional upload of sensitive information such as a legal contract, source code of a software product, or proprietary information, among others, can significantly enhance the risks involved.

The ramifications can be harsh, ranging from financial loss to reputational damage, and even legal implications for the organizations.

Therefore, a clear and comprehensive data security policy is critical to prevent potential hazards.

5. Augmentation Of Bias Already Exist

As promising as it sounds, Generative AI is not impervious to pre-existing biases that can be amplified through their use.

Data used for training Language Learning Models (LLMs) may inadvertently incorporate unconscious bias outside of the control of entities that utilize these models to accomplish specific tasks.

It is crucial for organizations that engage in AI development to seek guidance from a diverse team of expert professionals tasked with identifying and rectifying underlying biases present in both data and models.

Greenstein advises businesses to be proactive in this regard as the consequences of failing to do so can lead to devastating outcomes.

6. Occupational Roles And Workplace Morale

According to Greenstein, principal and the US Leader of PwC's AI, Data, and Analytics Strategy AI can replicate the daily tasks of knowledge workers such as content creation, coding, writing, summarization, and analysis.

Generative AI technologies are driving the displacement and replacement of human workers at an unprecedented pace. Ethical companies are investing in preparing their workforce for new roles created by generative AI applications.

To thrive amidst technological disruption, businesses must invest in developing their employees' generative AI skills

Taking proactive measures will minimize negative impacts and prepare companies for growth

7. Data Provenance

Generative AI is a double-edged sword as it relies heavily on vast amounts of data that can be poorly regulated and have shaky origins.

Lack of proper consent and underlying biases are ethical issues that are amplified by social influencers or AI systems themselves

The accuracy of Generative AI systems hinges upon the quality and sources of the underlying corpus of data

Synthetic data generated by generative AI is labeled as such for precise tracking of its usage

The generated data is shelved unless explicitly authorized and merely used as test data, never capitalized on for constructing future models or machine learning algorithms.

8. Shortage Of Transparency And Interpretive Capacity

Generative AI systems function by probabilistically grouping facts together based on how AI has learned to associate data elements with each other.

However, this learning process does not reveal all the details, resulting in some concerns about the trustworthiness of the data.

When analyzing generative AI outcomes, experts look for causal explanations for the results.

However, machine learning models and generative AI are based on searching for correlations, not causality. To address this, model interpretability is necessary to understand why a model delivers a particular result and distinguish whether it is a plausible explanation or not.

Therefore, organizations should not rely on generative AI systems to provide answers that could significantly impact individuals' lives and livelihoods due to the lack of trustworthiness and the absence of causal explanations. Until generative AI models can provide reliable explanations for their outputs, they must be approached with caution.

Ethical Best Practices for Generative AI

The new era of Generative AI has brought about unprecedented technological advancements that hold the potential for tremendous benefits to society when deployed in a controlled and regulated manner. However, the absence of strict adherence to ethical best practices can lead to significant harm and must be addressed.

One crucial step in adopting such ethical standards is staying informed and taking action. An Individual contributor or organizational leaders must immerse themselves in the current and future landscape of data ethics and diligently understand the guidelines for applying them.

Moreover, it is necessary to align with global standards to promote ethical best practices in Generative AI. The UNESCO AI ethics guidelines adopted by all 193 Member States, emphasize the crucial importance of human rights and dignity, peaceful and just societies, diversity and inclusiveness, and environmental flourishing. The guidelines also outline ten principles that can guarantee the safety, privacy, and transparency of using Generative AI models, among other essential pillars.

Furthermore, the guidelines offer policy action areas to promote ethical best practices in data governance, environment, gender, education, research, health, and social well-being. Therefore, adherence to global ethical standards is the most critical step toward the responsible deployment of Generative AI models.

Despite the enormous potential of Generative AI to revolutionize sectors such as healthcare and education by boosting productivity and creating fresh content, the technology comes with a set of significant ethical implications that cannot be ignored.

These ethical challenges include copyright violations, data privacy infringement, dissemination of harmful content, and magnification of pre-existing biases. Hence, as mankind continues to explore this powerful technology, society must adopt ethical best practices to ensure its responsible use.

Frequently Asked Questions

1. What is Generative AI and how does it work?

Generative AI is a subfield of artificial intelligence that involves training a computer system to generate new data or content based on its analysis of existing data.

It works by using algorithms to analyze patterns and relationships in datasets and then using this knowledge to generate new images, text, or other content.

2. What are some of the ethical concerns surrounding Generative AI?

There are several ethical concerns related to Generative AI, including potential copyright violations, data privacy infringement, dissemination of harmful content, and magnification of pre-existing biases.

These concerns arise due to the power of Generative AI to create realistic and convincing content that may be difficult to distinguish from human-created content.

3. How can professionals promote ethical best practices in Generative AI?

To promote ethical best practices in Generative AI, it is essential to align with global standards and guidelines, such as the UNESCO AI ethics guidelines.

These guidelines emphasize the importance of human rights, diversity, and inclusiveness, and outline ten principles to ensure the safety, privacy, and transparency of using Generative AI models.

Additionally, policymakers can take action to promote ethical best practices in areas such as data governance, environment, gender, education, research, health, and social well-being.

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