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AI ethics

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Muhammad Faizan
| September 28

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.

LLM robot

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. 


Cracks in the Facade: Flaws of Large Language Models | Data Science Dojo

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. 

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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


AI guardrail system

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. 


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Ruhma Khawaja author
Ruhma Khawaja
| September 22

People management in AI is the art of blending technical brilliance with human ingenuity to drive innovation and create a brighter future.


As technology continues to advance at an unprecedented rate, AI is rapidly transforming the way we live and work. From automated customer service to predictive analytics, AI is becoming an increasingly vital part of many industries. However, as the use of AI becomes more widespread, it’s important to consider the ethical implications of this technology. AI has the potential to perpetuate biases and reinforce systemic inaequalities if not designed and implemented thoughtfully. 

 In this blog, we will explore some of the key ethical consaiderations surrounding AI, including the importance of transparency, accountability, and diversity in AI development and deployment. By understanding these ethical considerations, we can ensure that AI is used to promote equality and benefit society. 

Key strategies for people management in AI: 

As AI continues to transform the workplace, the role of people management is becoming increasingly important. Managing AI teams requires a unique skill set that combines technical expertise with effective leadership and communication. Here are some key strategies for people management in AI: 


1. Hire the right people:  

The success of your AI team depends on hiring the right people. Look for candidates with a strong technical background in AI and machine learning, but also consider soft skills such as communication, teamwork, and adaptability. 

2. Provide clear direction 

 It’s important to provide clear direction for your AI team, including setting goals and expectations, outlining roles and responsibilities, and establishing communication channels. This can help ensure that everyone is on the same page and working towards the same objectives. 

3. Foster a culture of innovation 

Innovation is a key component of AI, so it’s important to foster a culture of innovation within your team. Encourage experimentation and creativity, and reward those who come up with new ideas or approaches. 

4. Develop technical and soft skills 

In addition to technical skills, AI team members also need strong soft skills such as communication, teamwork, and problem-solving. Provide opportunities for training and development in both technical and soft skills to help your team members grow and succeed. 

5. Encourage collaboration 

AI projects often involve multiple stakeholders, including developers, data scientists, business leaders, and end-users. Encourage collaboration and communication among these groups to ensure that everyone is working towards the same goals and that the end result meets the needs of all stakeholders. 

6. Embrace diversity 

Diversity is important in any workplace, and it’s especially important in AI. Encourage diversity in hiring and make sure that all team members feel valued and included. This can lead to more innovative solutions and better outcomes for your projects. 

7. Stay up-to-date 

AI is a rapidly evolving field, so it’s important to stay up-to-date on the latest trends and technologies. Encourage your team members to attend conferences, participate in online communities, and pursue ongoing education to stay on the cutting edge of AI.

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Significance of people management in AI

In today’s rapidly evolving business landscape, data is no longer just a competitive advantage but a necessity. Businesses rely on technology and data-driven predictive intelligence for critical decisions related to finance, marketing, customer support, and sales.

However, the traditional approach to managing human resources, which involves decision-making on recruitment, development, retention, and motivation, is evolving. Instead of relying solely on data analytics, AI is emerging as a valuable tool in the realm of people management.


Read more about -> 10 innovative ways to monetize business using ChatGPT


Top people management software solutions

Efficient people management is crucial for an organization’s growth and employee well-being. With the help of advanced management technology, a seamless HR system can be implemented to facilitate collaboration, streamline processes, and enhance employee engagement.

A comprehensive people management solution brings an entire team together under one reliable system, eliminating communication barriers, simplifying goal setting and tracking, providing detailed performance reports, and employing effective coaching methods to nurture employees’ skills.

In terms of user interface, functionality, cost, and overall customer satisfaction, these solutions stand out as top-tier people management systems in the industry.

1. Trakstar

Trakstar is a fully autonomous cloud-based solution that handles various people management tasks, including recruitment, mentoring, performance monitoring, and employee satisfaction. It equips HR managers with the tools needed to streamline personnel management processes, from hiring to an employee’s departure.

The platform offers a robust performance management system that encourages company-wide contributions. Managers gain access to visually rich reports filled with valuable data, allowing them to identify top performers, compare staff performance, and pinpoint areas for improvement.

2. Rippling

Rippling excels in people management with its exceptional procurement, straightforward tracking, and comprehensive reporting tools. The platform simplifies and automates the entire employee lifecycle, from recruitment to onboarding.

With just one click, Rippling enables you to post job openings on multiple online job sites, including Indeed and LinkedIn. The platform’s learning management system is also highly efficient.

3. Monday.com

While renowned as a workflow management application, Monday.com offers powerful integrated HR features. It is well-suited for managing employees, handling recruitment, facilitating onboarding, and supporting employee development.

Users can create tasks, assign them to teams, track processing times, and generate reports on various key performance indicators (KPIs). Customizable statistics and dashboards make it easy for HR managers to carry out their responsibilities. Automation capabilities simplify various essential processes, and the platform seamlessly integrates with other tools like Slack, Jira, Trello, GitHub, and more.

4. Lattice

Lattice is a smart people management solution that emphasizes engagement and employee growth. It features a 360-degree feedback tool that enables peers and managers to evaluate an employee’s performance. Lattice empowers managers to foster a culture of reliable and open feedback, where employees are recognized for their outstanding work.

The platform provides insights that inform organizations about their employees’ key strengths and areas for potential growth. Real-time goal setting, tracking, and management are made easy with Lattice. The application also facilitates meaningful 1:1 sessions between managers and employees, focusing on topics such as objectives, feedback, and growth strategies.

5. Zoho People

Zoho People offers user-friendly software designed to overcome communication barriers, support employee development, and enhance overall effectiveness. The platform creates virtual channels that capture important conversations between employees, teams, and organizations.

Managers can provide constructive feedback to employees using Zoho People’s streamlined performance appraisal process. Online conversations and an electronic timesheet system help facilitate conflict resolution.

With Zoho, managers can establish goals, track performance, assess team professionalism, and design training initiatives that foster individual growth.


Read more –> FraudGPT: Evolution of ChatGPT into an AI weapon for cybercriminals in 2023

Advantages of people management in AI 

Building strong AI teams through effective people management strategies can provide several advantages, including: 

  • Increased innovation: By fostering a culture of experimentation and creativity, AI teams can generate new ideas and solutions that may not have been possible with a more rigid approach.
  •  Enhanced collaboration: Effective people management strategies can encourage collaboration and communication within the team, leading to a more cohesive and productive work environment.
  • Improved diversity and inclusion: Prioritizing diversity and inclusion in AI teams can bring a range of perspectives and experiences to the table, leading to more innovative and effective solutions. 
  • Better decision-making: By ensuring transparency and accountability in AI development and deployment, organizations can make more informed and responsible decisions about how to use AI to benefit society. 
  • Improved project outcomes: By hiring the right people with the necessary skills and providing ongoing training and development, AI teams can deliver better outcomes for AI projects. 
  • Competitive advantage: Building strong AI teams can give organizations a competitive edge in their industry by enabling them to leverage AI more effectively and efficiently.Overall, effective people management strategies are essential for building strong AI teams that can harness the full potential of AI to drive innovation and create positive change in society. 

In a nutshell 

In conclusion, people management in AI requires a unique skill set that combines technical expertise with effective leadership and communication. By hiring the right people, providing clear direction, fostering a culture of innovation, developing technical and soft skills, encouraging collaboration, embracing diversity, and staying up-to-date, you can help your AI team succeed and achieve its goals. 


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Data Science Dojo
Ayesha Saleem
| October 4

The use of AI in culture raises interesting ethical reflections termed AI ethics nowadays.  

In 2016, a Rembrandt painting, “The Next Rembrandt”, was designed by a computer and created by a 3D printer, 351 years after the painter’s death.  

The achievement of this artistic prowess became possible when 346 Rembrandt paintings were together analyzed. The keen analysis of paintings pixel by pixel resulted in an upscale of deep learning algorithms to create a unique database.  

AI ethics - Rembrandt painting
Ethical dilemma of AI- Rembrandt’s painting

Every detail of Rembrandt’s artistic identity could then be captured and set the foundation for an algorithm capable of creating an unprecedented masterpiece. To bring the painting to life, a 3D printer recreated the texture of brushstrokes and layers of paint on the canvas for a breath-taking result that could trick any art expert. 

The ethical dilemma arose when it came to crediting the author of the painting. Who could it be?

Curious about how generative AI is reshaping the creative industry and what it means for artists and creators? Watch this podcast now!

We cannot overlook the transformations brought by intelligent machine systems in today’s world for the better. To name a few, artificial intelligence contributed to optimizing planning, detecting fraud, composing art, conducting research, and providing translations. 

Undoubtedly, it all contributed to the more efficient and consequently richer world of today. Leading global tech companies emphasize adopting a boundless landscape of artificial intelligence and step ahead of the competitive market.  

Amidst the boom of overwhelming technological revolutions, we cannot undermine the new frontier for ethics and risk assessment.  

Regardless of the risks AI offers, many real-world problems are begging to be solved by data scientists. Check out this informative session by Raja Iqbal (Founder and lead instructor at Data Science Dojo) on AI For Social Good 

Some of the key ethical issues in AI you must learn about are: 

1. Privacy & surveillance – Is your sensitive information secured?

Access to personally identifiable information must only be accessible to authorized users only. The other key aspects of privacy to consider in artificial intelligence are information privacy, privacy as an aspect of personhood, control over information about oneself, and the right to secrecy. 

Business today is going digital. We are associated with the digital sphere. Most digital data available online connects to a single Internet. There is increasingly more sensor technology in use that generates data about non-digital aspects of our lives. AI not only contributes to data collection but also drives possibilities for data analysis.  

Privacy and surveillance - AI ethics
Fingerprint scan, Privacy and surveillance – Data Science Dojo

Much of the most privacy-sensitive data analysis today–such as search algorithms, recommendation engines, and AdTech networks–are driven by machine learning and decisions by algorithms. However, as artificial intelligence evolves, it defines ways to intrude privacy interests of users.

For instance, facial recognition introduces privacy issues with the increased use of digital photographs. Machine recognition of faces has progressed rapidly from fuzzy images to rapid recognition of individual humans.  

2. Manipulation of behavior – How does the internet know our preferences?

The use of the internet and online activities keeps us engaged every day. We do not realize that our data is constantly collected, and information is tracked. Our personal data is used to manipulate our behavior online and offline as well.  

If you are thinking about exactly when businesses make use of the information gathered and how they manipulate us, then marketers and advertisers are the best examples. To sell the right product to the right customer, it is significant to know the behavior of your customer.

Their interests, past purchase history, location, and other key demographics. Therefore, advertisers retrieve the personal information of potential customers that is available online. 

AI Ethics - User behaviour
Behavior manipulation- AI ethics, Data Science Dojo

Social media has become the hub of manipulating user behaviors by marketers to maximize profits. AI with its advanced social media algorithms identifies vulnerabilities in human behavior and influences our decision-making process. 

 Artificial intelligence integrates such algorithms with digital media that exploit human biases detected by AI algorithms. It implies personalized addictive strategies for consumption of (online) goods or benefits from the vulnerable state of individuals to promote products and services that match well with their temporary emotions. 

3. Opacity of AI systems – Complexed AI processes

Danaher stated, “we are creating decision-making processes that constrain and limit opportunities for human participation” 

Artificial Intelligence supports automated decision-making, thus neglecting the free will of personnel to speak of their choice. AI processes work in a way that no one knows how the output is generated. Therefore, the decision will remain opaque even for the experts  

AI systems use machine learning techniques in neural networks to retrieve patterns from a given dataset. With or without “correct” solutions provided, i.e., supervised, semi-supervised or unsupervised.


Read this blog to learn more about AI powered document search


Machine learning captures existing patterns in the data with the help of these techniques. And then label these patterns in such a way that it gets useful for the decision the system makes, while the programmer does not really know which patterns in the data the system has used. 

4. Human-robot interaction – Are robots more capable than us?

As AI is now widely used to manipulate human behavior, it is also actively driving robots. It can get problematic if their processes or appearance involve deception or threatening human dignity 

The key ethical issue here is, “Should robots be programmed to deceive us?” If we answer this question with a yes, then the next question to ask is “What should be the limits of deception?” If we say that robots can deceive us if it does not seriously harm us, then the robot might lie about its abilities or pretend to have more knowledge than it has.  

human robot - AI ethics
Human-robot interaction- Data Science Dojo

If we believe that robots should not be programmed to deceive humans, then the next ethical question becomes “Should robots be programmed to lie at all?” The answer would depend on what kind of information they are giving and whether humans can provide an alternative source.  

Robots are now being deployed in the workplace to do jobs that are dangerous, difficult, or dirty. The automation of jobs is inevitable in the future, and it can be seen as a benefit to society or a problem that needs to be solved. The problem arises when we start talking about human-robot interaction and how robots should behave around humans in the workplace. 

5. Autonomous systems – AI gaining self-sufficiency

An autonomous system can be defined as a self-governing or self-acting entity that operates without external control. It can also be defined as a system that can make its own decisions based on its programming and environment. 

The next step in understanding the ethical implications of AI is to analyze how it affects society, humans, and our economy. This will allow us to predict the future of AI and what kind of impact it will have on society if left unchecked. 

In societies where AI is rapidly replacing humans can get harmed or suffer in the longer run. For instance, thinking of AI writers as a replacement for human copywriters when it is just designed to bring efficiency to a writer’s job, assist, and help in getting rid of writer’s block while generating content ideas at scale.  

Secondly, autonomous vehicles are the most relevant examples for a heated debate topic of ethical issues in AI. It is not yet clear what the future of autonomous vehicles will be. The main ethical concern around autonomous cars is that they could cause accidents and fatalities. 

Some people believe that because these cars are programmed to be safe, they should be given priority on the road. Others think that these vehicles should have the same rules as human drivers. 

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6. Machine ethics – Can we infuse good behavior in machines?

Before we get into the ethical issues associated with machines, we need to know that machine ethics is not about humans using machines. But it is solely related to the machines operating independently as subjects. 

The topic of machine ethics is a broad and complex one that includes a few areas of inquiry. It touches on the nature of what it means for something to be intelligent, the capacity for artificial intelligence to perform tasks that would otherwise require human intelligence, the moral status of artificially intelligent agents, and more. 


Read this blog to learn about Big Data Ethics


The field is still in its infancy, but it has already shown promise in helping us understand how we should deal with certain moral dilemmas. 

In the past few years, there has been a lot of research on how to make AI more ethical. But how can we define ethics for machines? 

AI programmed machines with rules for good behavior and to avoid making bad decisions based on the principles. It is not difficult to imagine that in the future, we will be able to tell if an AI has ethical values by observing its behavior and its decision-making process. 

Three laws of robotics by Isaac for machine ethics are: 

First Law—A robot may not injure a human being or, through inaction, allow a human being to come to harm.  

Second Law—A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.  

Third Law—A robot must protect its own existence if such protection does not conflict with the First or Second Laws. 

Artificial Moral Agents 

The development of artificial moral agents (AMA) is a hot topic in the AI space. The AMA has been designed to be a moral agent that can make moral decisions and act according to these decisions. As such, it has the potential to have significant impacts on human lives. 

The development of AMA is not without ethical issues. The first issue is that AMAs (Artificial Moral Agents) will have to be programmed with some form of morality system that could be based on human values or principles from other sources.  

This means that there are many possibilities for diverse types of AMAs and several types of morality systems, which may lead to disagreements about what an AMA should do in each situation. Secondly, we need to consider how and when these AMAs should be used as they could cause significant harm if they are not used properly 

Closing on AI ethics 

Over the years, we went from, “AI is impossible” (Dreyfus 1972) and “AI is just automation” (Lighthill 1973) to “AI will solve all problems” (Kurzweil 1999) and “AI may kill us all” (Bostrom 2014). 

Several questions arise with the increasing dependency on AI and robotics. Before we rely on these systems further, we must have clarity about what the systems themselves should do, and what risks they have in the long term.  

Let us know in the comments if you also think it also challenges the human view of humanity as the intelligent and dominant species on Earth.  

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