Risks in Generative AI and their Impact on Businesses 

There is no denying the fact that the usage of Generative AI (Gen AI) has revolutionized the way we deal with information. Be it healthcare, software development, space science, business, marketing, sales or education, Gen AI is adding value and enabling faster yet more efficient completion of tasks. However, use of GenAI comes with its own set of challenges and problems which, if not dealt with efficiently and proactively, can be detrimental to the organization’s reputation. 

Common risks related with the use of GenAI include: 

• Bias and discrimination 
• Misinformation and hallucination 
• Toxic language and harmful content 
• Privacy and security concerns 
• Ethical and legal challenges 
• Deepfakes and synthetic media

Let’s explore these challenges in depth, understand their potential impact on your business, and identify effective strategies to address them. 

What is AI bias? 

AI bias is also known as machine learning bias or algorithm bias. It is the occurrence of biased results produced by Generative AI engines. At the core of AI bias lies flawed, opinionated and skewed training data or even AI algorithm. You provide biased training; you will get distorted results. 

What are the common examples of AI induced biases? 

With the widespread applications of GenAI, biases emerge in almost every aspect of human life. Some of the commonly visible biases are as follows- 

• AI bias in facial recognition technology: Misidentification of one ethnic group over another by the facial recognition technology, primarily due to faulty training data. 
• Gender bias in AI models: Popular AI language models like GPT are known to display gender bias. For example, GPTs are more likely to associate male names with career and female names with family related terms.
• Bias in healthcare: Faulty AI algorithms in healthcare management systems rarely referred one ethnic group for secondary care over another despite being sicker. Read more in our blog: AI-Enabled Healthcare – How Quality Engineering Needs to Evolve to Meet New Challenges 
• Algorithmic bias in hiring: A Harvard study shows that AI-driven hiring models are biased against underrepresented groups. Even HR managers agree that this bias can be perpetuated by AI models. 
• Social media bias: Often, AI models deployed to detect hate speeches are less likely to detect speech directed at women or minority groups.

Why is it important to address AI bias? 

When AI bias goes unaddressed, it hinders people’s participation in the economy and society. In fact, when AI produces biased results, it erodes people’s trust in the authenticity and reliability of GenAI.  It fosters mistrust among users. 

Impact of AI bias on businesses 

Businesses are also at the receiving end of AI Biases and risk the loss of reputation. As per the IBM Global AI Adoption Index 2021, 85% of the organizations surveyed recognize that bias in AI can result in damage to brand reputation and customer trust. In addition, 68% of the organizations are apprehensive about legal implications resulting from biased AI systems. 

Let us look at the statistics highlighting the impact of biased AI systems on businesses worldwide. 

Statistic	Value	Source
Companies that experienced customer backlash due to biased AI decisions	34%	PwC Responsible AI Survey, 2022
Executives worried about reputational damage from AI bias	78%	BCG Henderson Institute, “AI & Bias: Impact on Brand and Trust,” 2023
AI bias resulting in unintended consequences affecting minority groups	56% of cases	Brookings Institution, “Understanding and Mitigating AI Bias,” 2023

The statistics are a clear indication that AI bias is a real problem for businesses and has the potential of causing customer backlash as well as loss to brand value.  

Hallucination and misinformation 

You must have heard the term ‘hallucination’ from a medical perspective. It refers to imaginary or false perception of events or objects involving your senses. When it happens within GenAI systems, it is called AI hallucination. 

Simply put, AI hallucination is a phenomenon where Large Language Models (LLMs), generally an AI chatbot, perceives patterns which are nonexistent or imaginary. These outputs might be imperceptible for human observers and might be totally inaccurate. 

When a user makes a request to a GenAI tool, it expects the output to address the queries or tasks defined in the prompt. However sometimes, the AI algorithm produces results which are not based on the training data. These outputs are a result of incorrect decoding by the transformer. This is AI hallucination. 

Some notable examples of AI hallucination are: 

• Google’s Bard claimed that the James Webb telescope had captured an image of a planet outside of our solar system
• Microsoft’ Chatbot Sydney, accepted falling in love with users and spying on Microsoft employees
• Meta had to close its LLM model Galactica after just three days after it provided inaccurate outputs rooted in prejudices

How does it impact businesses? 

AI hallucinations may lead to an unprecedented loss of reputation for business. Here are some of the ways in which AI hallucinations can affect business. 

Increased operational cost: AI hallucinations require businesses to implement increased oversight and correction, continuous monitoring and additional review mechanisms. An estimate by McKinsey & Company found that AI-related errors can lead to significant costs in industries such as healthcare and finance.

Regulatory scrutiny and compliance risks: Organizations can be under the radar of regulatory authorities if misinformation (Supplied by the GenAI systems) results in consumer harm. In addition to this, regulatory authorities are increasingly focusing on AI accountability and transparency. AI hallucinations can put businesses at the risk of non-compliance with policies like GDPR (General Data Protection Regulation) of EU. 

Loss of trust and bad consumer experience: If a chatbot is providing inaccurate information and biased results, this will ultimately result in the loss of trust and a bad consumer experience. 

Negative impact on business decisions: Relying on fabricated or hallucinated data can lead businesses to make poor business decisions or strategic errors. 

Toxic language and harmful content 

Much like the adage, ‘Garbage in, garbage out,’ the quality of any output is inherently tied to the quality of input data. As GenAI models are trained on a vast dataset, they remain vulnerable. The sources of input data include the internet, articles, books and social media, where toxic language and prejudice have, sadly, existed for a long time now. Especially if social media is considered, hate speeches, bullying and demeaning language has been a perpetual challenge and when this finds its way into the LLMs, they result in outputs with equally toxic languages. 

One more aspect of this issue is that the generative models do not understand the intent or context and they may generate results which are harmful, inflammatory or inappropriate. 

Some of the examples of toxic outputs by GenAI are: 

• Hate speech.
• Text reflecting gender and religious bias.
• Racist or misogynistic language.
• Fabricated or false news including ‘deep fakes’.
• Conspiracy theories.

How toxic results from GenAI can impact businesses 

Negative publicity: Even if the GenAI results are inadvertently produced, the offending organization might face customer backlash, and loss of reputation. 

Regulatory risks: As authorities are increasingly aware of such instances, they require organizations to be extra vigilant. The European Union’s Digital Services Act is noteworthy in this regard. It mandates that social media platforms address illegal content in a timely manner. 

Privacy and security concerns 

GenAI applications are based on underlying Large Language Models (LLMs). Technically, the LLMs are the text generation part of the GenAI. These models require a vast amount of data for training and fine tuning. This huge amount of data also contains proprietary business data, or confidential documents related to businesses. 

Without tight oversight, AI systems can reveal or expose private data, leading to privacy and security risks. In addition, the AI systems can learn from and reproduce sensitive data patterns resulting in data leaks. 

It is pertinent to highlight that AI models, especially trained from scraped datasets from the internet, are more vulnerable to privacy breaches. 

Research has shown that AI models like GPT can memorize and reproduce private data found in training data. It would not be an exaggeration to say that GPTs can reveal personal data like credit card numbers, email ID or phone numbers in response to user prompts. AI systems trained for face recognition are often trained on images scraped from social media without consent. 

What is the impact of privacy and security concerns due to AI on businesses? 

If AI systems inadvertently leak private information, organizations might have to incur significant losses in the form of fines, compensation and legal fees. In addition to this, a data breach can put the organization on the regulatory radar. Compounding the long-term implications for brand reputation would be the loss of customer trust and confidence. 

Ethical and legal implications 

GenAI systems like language models face serious legal and ethical challenges. These challenges revolve around issues like copyright infringement and violation of intellectual property rights. As part of their training data, the AI models have been trained on a vast amount of data including copyrighted material. This raises serious concerns related to fairness, reliability and accountability. 

Furthermore, when AI systems are used in making decisions in areas such as recruitment, healthcare or law enforcement, the logic to arrive at a decision lacks transparency.  This opacity can create ethical dilemmas and mistrust. Unfair or biased decisions in such domains can have profound consequences.  

Examples of ethical concerns related to GenAI 

• AI models like Dall-E, GPT and others are often trained on data scraped from internet, which contains copyrighted text, images and videos.
• Case of Clearview where the firm faced legal challenges for using billions of images without consent for facial recognition purposes.

What is the impact of ethical and moral challenges posed by GenAI? 

Organizations using proprietary data without consent may face legal challenges. Legal actions can lead to hefty fines, penalties and bans from specific markets. The usage of private data without consent might lead to loss of consumer’s trust in the brand. Companies failing to comply with regulations like GDPR or AI act might face restrictions, fines and investigations. 

Deepfakes and synthetic data 

Deepfakes and other AI generated synthetic media represents a significant, often sinister, threat. It is one of the most potent toxicities attached with the use of GenAI. Deepfakes are hyper realistic images, videos and audio prepared with the help of intelligent AI algorithms. 

Deepfake creators generally use Generative Adversarial Networks or GANs. Deepfakes are experts at mimicking voice, behavior and likeness of the individuals concerned. It makes it very difficult, even for the trained professionals, to detect its origin. 

Though this capability of AI can be efficiently used for creative purposes, it is being used for malicious activities like identity theft, misinformation and fraud. 

How do deepfakes impact businesses? 

Cybersecurity risks: Deepfakes have challenged the traditional cybersecurity measures of organizations. Using deepfakes, cyber fraudsters can mimic facial or voice samples and get entry into the seemingly secure databases of companies. Deepfakes can also be used to manipulate employees in disclosing sensitive information which ca be harmful for the organizations. 

Threat to brand reputation: Deepfakes can be used to target senior leaders of an organization, and it can severely damage its reputation. 

Generative AI Assurance for identification of risks 

The mentioned toxicities present a solid challenge in front of the GenAI users and businesses across the world. It is critical for an organization to design and implement AI Assurance framework that will enable identification of possible risks before releasing to customers. The following are areas can be considered for assurance. 

Bias and discrimination: 

• Conduct continuous audits of AI models to identify and rectify biases. 
• Use diversified training datasets to help reduce inherent bias. 
• Implement bias detection tools and regular monitoring mechanisms.

Hallucination and misinformation 

• Keep humans in the loop for regular audit and verification. 
• Build a robust validation process for AI generated outputs. 
• Ensure accuracy of data using multiple credible sources.

Privacy and security concerns 

• Anonymize data to protect identity and confidentiality. 
• Ensure advanced and secure data handling processes to prevent unauthorized access. 
• Conduct regular compliance and regulatory audits to adhere to GDPR and other guidelines.

Toxic language and harmful content 

• Ensure toxicity detection algorithms are in place to screen outputs. 
• Enforce strict ethical guidelines for AI deployments. 
• Update training data periodically to exclude harmful content.

Deepfakes and synthetic media 

• Deploy advanced deepfake detection tools to flag and identify fake information. 
• Train employees to identify and respond to deepfake frauds. 
• Strengthen cybersecurity measures for protection against deepfakes.

How Qualitest can help 

Generative AI is transforming industries by driving innovation and growth. However, it also brings risks such as hallucinations, bias, toxicity, and privacy concerns. At Qualitest, we specialize in Quality Engineering that ensures your GenAI-infused applications are robust, reliable, and aligned with your business goals. 

Our continuous research and thought leadership in GenAI led us to build AI Assurance solutions.  Through implementation of these solutions and rigorous testing of Large Language Models (LLMs), text-to-text, and text-to-image applications; we proactively identify and mitigate issues such as toxicity, hallucinations, and bias to protect your brand and enhance the user experience.

With Qualitest, you gain:

• Reduced hallucinations and bias: We apply advanced AI testing methodologies to detect and remove hallucinations, ensuring your AI systems provide accurate, factual, and unbiased outputs. 
• Enhanced data quality and privacy: Our end-to-end data quality framework and privacy protection strategies ensure data integrity and compliance from source to deployment. 
• Faster, accurate global releases: Our localization testing validates your AI applications across geographies, removing cultural bias and ensuring accuracy in regional contexts. 
• Automated validation and continuous improvement: Through automated validation and adversarial testing, we continually benchmark your AI models against industry standards, optimizing performance and safeguarding against threats.

Transform your business with GenAI while safeguarding it against potential pitfalls. Let Qualitest be your partner in delivering AI solutions that are reliable, ethical, and future proof. Contact us today to build a stronger, smarter AI strategy!