ethical ai

AI disasters caused notable instances where the application of AI has led to negative consequences or exacerbations of pre-existing issues.

Artificial Intelligence (AI) has a multifaceted impact on society, ranging from the transformation of industries to ethical and environmental concerns. AI holds the promise of revolutionizing many areas of our lives by increasing efficiency, enabling innovation, and opening up new possibilities in various sectors.

The growth of the AI market is only set to boom. In fact, McKinsey projects an economic impact of $6.1-7.9T annually.

One significant impact of AI is on disaster risk reduction (DRR), where it aids in early warning systems and helps in projecting potential future trajectories of disasters. AI systems can identify areas susceptible to natural disasters and facilitate early responses to mitigate risks.

However, the use of AI in such critical domains raises profound ethical, social, and political questions, emphasizing the need to design AI systems that are equitable and inclusive.

AI also affects employment and the nature of work across industries. With advancements in generative AI, there is a transformative potential for AI to automate and augment business processes, although the technology is still maturing and cannot yet fully replace human expertise in most fields.

Moreover, the deployment of AI models requires substantial computing power, which has environmental implications. For instance, training and operating AI systems can result in significant CO2 emissions due to the energy-intensive nature of the supporting server farms.

Consequently, there is growing awareness of the environmental footprint of AI and the necessity to consider the potential climate implications of widespread AI adoption.

In alignment with societal values, AI development faces challenges like ensuring data privacy and security, avoiding biases in algorithms, and maintaining accessibility and equity. The decision-making processes of AI must be transparent, and there should be oversight to ensure AI serves the needs of all communities, particularly marginalized groups.

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That said, let’s have a quick look at the 5 most famous AI disasters that occurred recently:

5 famous AI disasters

AI is not inherently causing disasters in society, but there have been notable instances where the application of AI has led to negative consequences or exacerbations of pre-existing issues:

Generative AI in legal research

An attorney named Steven A. Schwartz used OpenAI’s ChatGPT for legal research, which led to the submission of at least six nonexistent cases in a lawsuit’s brief against Colombian airline Avianca.

The brief included fabricated names, docket numbers, internal citations, and quotes. The use of ChatGPT resulted in a fine of $5,000 for both Schwartz and his partner Peter LoDuca, and the dismissal of the lawsuit by US District Judge P. Kevin Castel.

Machine learning in healthcare

AI tools developed to aid hospitals in diagnosing or triaging COVID-19 patients were found to be ineffective due to training errors.

The UK’s Turing Institute reported that these predictive tools made little to no difference. Failures often stem from the use of mislabeled data or data from unknown sources.

An example includes a deep learning model for diagnosing COVID-19 that was trained on a dataset with scans of patients in different positions and was unable to accurately diagnose the virus due to these inconsistencies.

AI in real estate at Zillow

Zillow utilized a machine learning algorithm to predict home prices for its Zillow Offers program, aiming to buy and flip homes efficiently.

However, the algorithm had a median error rate of 1.9%, and, in some cases, as high as 6.9%, leading to the purchase of homes at prices that exceeded their future selling prices.

This misjudgment resulted in Zillow writing down $304 million in inventory and led to a workforce reduction of 2,000 employees, or approximately 25% of the company.

Bias in AI recruitment tools:

Amazon’s case is not detailed in the provided sources, but referencing similar issues of bias in recruitment tools, it’s notable that AI algorithms can unintentionally incorporate biases from the data they are trained on.

In AI recruiting tools, this means if the training datasets have more resumes from one demographic, such as men, the algorithm might show preference to those candidates, leading to discriminatory hiring practices.

AI in recruiting software at iTutorGroup:

iTutorGroup’s AI-powered recruiting software was programmed with criteria that led it to reject job applicants based on age. Specifically, the software discriminated against female applicants aged 55 and over, and male applicants aged 60 and over.

This resulted in over 200 qualified candidates being unfairly dismissed by the system. The US Equal Employment Opportunity Commission (EEOC) took action against iTutorGroup, which led to a legal settlement. iTutorGroup agreed to pay $365,000 to resolve the lawsuit and was required to adopt new anti-discrimination policies as part of the settlement.

Ethical concerns for organizations – Post-deployment of AI

The use of AI within organizations brings forth several ethical concerns that need careful attention. Here is a discussion on the rising ethical concerns post-deployment of AI:

Data Privacy and Security:

The reliance on data for AI systems to make predictions or decisions raises significant concerns about privacy and security. Issues arise regarding how data is gathered, stored, and used, with the potential for personal data to be exploited without consent.

Bias in AI:

When algorithms inherit biases present in the data they are trained on, they may make decisions that are discriminating or unjust. This can result in unfair treatment of certain demographics or individuals, as seen in recruitment, where AI could prioritize certain groups over others unconsciously.

Accessibility and Equity:

Ensuring equitable access to the benefits of AI is a major ethical concern. Marginalized communities often have lesser access to technology, which may leave them further behind. It is crucial to make AI tools accessible and beneficial to all, to avoid exacerbating existing inequalities.

Accountability and Decision-Making:

The question of who is accountable for decisions made by AI systems is complex. There needs to be transparency in AI decision-making processes and the ability to challenge and appeal AI-driven decisions, especially when they have significant consequences for human lives.

Overreliance on Technology:

There is a risk that overreliance on AI could lead to neglect of human judgment. The balance between technology-aided decision-making and human expertise needs to be maintained to ensure that AI supports, not supplants, human roles in critical decision processes.

Infrastructure and Resource Constraints:

The implementation of AI requires infrastructure and resources that may not be readily available in all regions, particularly in developing countries. This creates a technological divide and presents a challenge for the widespread and fair adoption of AI.

These ethical challenges require organizations to establish strong governance frameworks, adopt responsible AI practices, and engage in ongoing dialogue to address emerging issues as AI technology evolves.

Tune into this podcast to explore how AI is reshaping our world and the ethical considerations and risks it poses for different industries and the society.

Watch our podcast Future of Data and AI here

How can organizations protect themselves from AI risks?

To protect themselves from AI disasters, organizations can follow several best practices, including:

Adherence to Ethical Guidelines:

Implement transparent data usage policies and obtain informed consent when collecting data to protect privacy and ensure security .

Bias Mitigation:

Employ careful data selection, preprocessing, and ongoing monitoring to address and mitigate bias in AI models .

Equity and Accessibility:

Ensure that AI-driven tools are accessible to all, addressing disparities in resources, infrastructure, and education .

Human Oversight:

Retain human judgment in conjunction with AI predictions to avoid overreliance on technology and to maintain human expertise in decision-making processes.

Infrastructure Robustness:

Invest in the necessary infrastructure, funding, and expertise to support AI systems effectively, and seek international collaboration to bridge the technological divide.

Verification of AI Output:

Verify AI-generated content for accuracy and authenticity, especially in critical areas such as legal proceedings, as demonstrated by the case where an attorney submitted non-existent cases in a court brief using output from ChatGPT. The attorney faced a fine and acknowledged the importance of verifying information from AI sources before using them.

One real use case to illustrate these prevention measures is the incident involving iTutorGroup. The company faced a lawsuit due to its AI-powered recruiting software automatically rejecting applicants based on age.

To prevent such discrimination and its legal repercussions, iTutorGroup agreed to adopt new anti-discrimination policies as part of the settlement. This case demonstrates that organizations must establish anti-discrimination protocols and regularly review the criteria used by AI systems to prevent biases.

Read more about big data ethics and experiments

Future of AI development

AI is not inherently causing disasters in society, but there have been notable instances where the application of AI has led to negative consequences or exacerbations of pre-existing issues.

Challenges of Large Language Models: LLMs are AI giants reshaping human-computer interactions, displaying linguistic marvels. However, beneath their prowess, lie complex challenges, limitations, and ethical concerns.

In the realm of artificial intelligence, LLMs have risen as titans, reshaping human-computer interactions, and information processing. GPT-3 and its kin are linguistic marvels, wielding unmatched precision and fluency in understanding, generating, and manipulating human language.

Photo by Rock’n Roll Monkey on Unsplash 

Yet, behind their remarkable prowess, a labyrinth of challenges, limitations, and ethical complexities lurks. As we dive deeper into the world of LLMs, we encounter undeniable flaws, computational bottlenecks, and profound concerns. This journey unravels the intricate tapestry of LLMs, illuminating the shadows they cast on our digital landscape. 

Neural wonders: How LLMs master language at scale 

At their core, LLMs are intricate neural networks engineered to comprehend and craft human language on an extraordinary scale. These colossal models ingest vast and diverse datasets, spanning literature, news, and social media dialogues from the internet.

Their primary mission? Predicting the next word or token in a sentence based on the preceding context. Through this predictive prowess, they acquire grammar, syntax, and semantic acumen, enabling them to generate coherent, contextually fitting text. This training hinges on countless neural network parameter adjustments, fine-tuning their knack for spotting patterns and associations within the data.

Challenges of large language models

Consequently, when prompted with text, these models draw upon their immense knowledge to produce human-like responses, serving diverse applications from language understanding to content creation. Yet, such incredible power also raises valid concerns deserving closer scrutiny. If you want to dive deeper into the architecture of LLMs, you can read more here. 

Ethical concerns surrounding large language models: 

Large Language Models (LLMs) like GPT-3 have raised numerous ethical and social implications that need careful consideration.

These transformative AI systems, while undeniably powerful, have cast a spotlight on a spectrum of concerns that extend beyond their technical capabilities. Here are some of the key concerns:  

1. Bias and fairness:

LLMs are often trained on large datasets that may contain biases present in the text. This can lead to models generating biased or unfair content. Addressing and mitigating bias in LLMs is a critical concern, especially when these models are used in applications that impact people’s lives, such as in hiring processes or legal contexts.

In 2016, Microsoft launched a chatbot called Tay on Twitter. Tay was designed to learn from its interactions with users and become more human-like over time. However, within hours of being launched, Tay was flooded with racist and sexist language. As a result, Tay began to repeat this language, and Microsoft was forced to take it offline. 

Read more –> Algorithmic biases – Is it a challenge to achieve fairness in AI?

2. Misinformation and disinformation:

LLMs can generate highly convincing fake news, disinformation, and propaganda. One of the gravest concerns surrounding the deployment of Large Language Models (LLMs) lies in their capacity to produce exceptionally persuasive counterfeit news articles, disinformation, and propaganda.

These AI systems possess the capability to fabricate text that closely mirrors the style, tone, and formatting of legitimate news reports, official statements, or credible sources. This issue was brought forward in this research. 

3. Dependency and deskilling:

Excessive reliance on Large Language Models (LLMs) for various tasks presents multifaceted concerns, including the erosion of critical human skills. Overdependence on AI-generated content may diminish individuals’ capacity to perform tasks independently and reduce their adaptability in the face of new challenges.

In scenarios where LLMs are employed as decision-making aids, there’s a risk that individuals may become overly dependent on AI recommendations. This can impair their problem-solving abilities, as they may opt for AI-generated solutions without fully understanding the underlying rationale or engaging in critical analysis.

4. Privacy and security threats:

Large Language Models (LLMs) pose significant privacy and security threats due to their capacity to inadvertently leak sensitive information, profile individuals, and re-identify anonymized data. They can be exploited for data manipulation, social engineering, and impersonation, leading to privacy breaches, cyberattacks, and the spread of false information.

LLMs enable the generation of malicious content, automation of cyberattacks, and obfuscation of malicious code, elevating cybersecurity risks. Addressing these threats requires a combination of data protection measures, cybersecurity protocols, user education, and responsible AI development practices to ensure the responsible and secure use of LLMs. 

5. Lack of accountability:

The lack of accountability in the context of Large Language Models (LLMs) arises from the inherent challenge of determining responsibility for the content they generate. This issue carries significant implications, particularly within legal and ethical domains.

When AI-generated content is involved in legal disputes, it becomes difficult to assign liability or establish an accountable party, which can complicate legal proceedings and hinder the pursuit of justice. Moreover, in ethical contexts, the absence of clear accountability mechanisms raises concerns about the responsible use of AI, potentially enabling malicious or unethical actions without clear repercussions.

Thus, addressing this accountability gap is essential to ensure transparency, fairness, and ethical standards in the development and deployment of LLMs. 

6. Filter bubbles and echo chambers:

Large Language Models (LLMs) contribute to filter bubbles and echo chambers by generating content that aligns with users’ existing beliefs, limiting exposure to diverse viewpoints. This can hinder healthy public discourse by isolating individuals within their preferred information bubbles and reducing engagement with opposing perspectives, posing challenges to shared understanding and constructive debate in society. 

Navigating the solutions: Mitigating flaws in large language models 

As we delve deeper into the world of AI and language technology, it’s crucial to confront the challenges posed by Large Language Models (LLMs). In this section, we’ll explore innovative solutions and practical approaches to address the flaws we discussed. Our goal is to harness the potential of LLMs while safeguarding against their negative impacts. Let’s dive into these solutions for responsible and impactful use. 

1. Bias and Fairness:

Establish comprehensive and ongoing bias audits of LLMs during development. This involves reviewing training data for biases, diversifying training datasets, and implementing algorithms that reduce biased outputs. Include diverse perspectives in AI ethics and development teams and promote transparency in the fine-tuning process.

Guardrails AI can enforce policies designed to mitigate bias in LLMs by establishing predefined fairness thresholds. For example, it can restrict the model from generating content that includes discriminatory language or perpetuates stereotypes. It can also encourage the use of inclusive and neutral language.

Guardrails serve as a proactive layer of oversight and control, enabling real-time intervention and promoting responsible, unbiased behavior in LLMs. You can read more about Guardrails for AI in this article by Forbes.  

Read more –> LLM Use-Cases: Top 10 industries that can benefit from using large language models

The architecture of an AI-based guardrail system

2.  Misinformation and disinformation:

Develop and promote robust fact-checking tools and platforms to counter misinformation. Encourage responsible content generation practices by users and platforms. Collaborate with organizations that specialize in identifying and addressing misinformation.

Enhance media literacy and critical thinking education to help individuals identify and evaluate credible sources. Additionally, Guardrails can combat misinformation in Large Language Models (LLMs) by implementing real-time fact-checking algorithms that flag potentially false or misleading information, restricting the dissemination of such content without additional verification.

These guardrails work in tandem with the LLM, allowing for the immediate detection and prevention of misinformation, thereby enhancing the model’s trustworthiness and reliability in generating accurate information. 

3. Dependency and deskilling:

Promote human-AI collaboration as an augmentation strategy rather than a replacement. Invest in lifelong learning and reskilling programs that empower individuals to adapt to AI advances. Foster a culture of responsible AI use by emphasizing the role of AI as a tool to enhance human capabilities, not replace them. 

4. Privacy and security threats:

Strengthen data anonymization techniques to protect sensitive information. Implement robust cybersecurity measures to safeguard against AI-generated threats. Developing and adhering to ethical AI development standards to ensure privacy and security are paramount considerations.

Moreover, Guardrails can enhance privacy and security in Large Language Models (LLMs) by enforcing strict data anonymization techniques during model operation, implementing robust cybersecurity measures to safeguard against AI-generated threats, and educating users on recognizing and handling AI-generated content that may pose security risks.

These guardrails provide continuous monitoring and protection, ensuring that LLMs prioritize data privacy and security in their interactions, contributing to a safer and more secure AI ecosystem. 

5. Lack of accountability:

Establish clear legal frameworks for AI accountability, addressing issues of responsibility and liability. Develop digital signatures and metadata for AI-generated content to trace sources.

Promote transparency in AI development by documenting processes and decisions. Encourage industry-wide standards for accountability in AI use. Guardrails can address the lack of accountability in Large Language Models (LLMs) by enforcing transparency through audit trails that record model decisions and actions, thereby holding AI accountable for its outputs. 

6. Filter bubbles and echo chambers:

Promote diverse content recommendation algorithms that expose users to a variety of perspectives. Encourage cross-platform information sharing to break down echo chambers. Invest in educational initiatives that expose individuals to diverse viewpoints and promote critical thinking to combat the spread of filter bubbles and echo chambers. 

In a nutshell 

The path forward requires vigilance, collaboration, and an unwavering commitment to harness the power of LLMs while mitigating their pitfalls.

By championing fairness, transparency, and responsible AI use, we can unlock a future where these linguistic giants elevate society, enabling us to navigate the evolving digital landscape with wisdom and foresight. The use of Guardrails for AI is paramount in AI applications, safeguarding against misuse and unintended consequences.

The journey continues, and it’s one we embark upon with the collective goal of shaping a better, more equitable, and ethically sound AI-powered world.