What Ethical Challenges Do AI and Big Data Pose for Businesses?
In the current digital world, AI and data-driven innovation have become essential across different industry verticals like travel, health, retail, e-commerce, manufacturing, and even government. Further, they have a huge positive impact on many areas of the economy as well as society at large. On the other hand, when they are not used in the right way, they can affect privacy and data protection laws.
The Emergence of AI and Big Data in Businesses
Earlier, data was gathered and stored to maintain records and make future decisions based on the outcome. For example, in the business world, purchase and sales data are kept on record to check the company’s profit and loss performance. Additionally, in medicine, patient data and prescription data were kept confidential to facilitate the seamless exchange of patient information among hospital departments. These and more such incidents served as the key reasons for “the paperless campaign,” which ultimately introduced information technologies to numerous firms.
There has been a huge quantity of data gathered throughout the years, and fresh data is continuously being gathered every day. Companies perceive the opportunity to achieve their objectives and decision-making processes by uncovering the hidden value in the data gathered over time. Still, new methods—namely, big data and artificial intelligence (AI)—have emerged when the amount of data exceeds the capacity of humans.
Giant tech corporations like Amazon, Facebook, IBM, Microsoft, and Alphabet, together with people like Elon Musk and Stephen Hawking, believe the moment is perfect to discuss the almost limitless possibilities of artificial intelligence. This is, in many respects, as much a new frontier for developing technology as it is for ethics and risk assessment. What social issues of artificial intelligence keep AI experts up at night? What are the unethical use of big data examples?
Control and Morality
Companies are becoming dependent on robots to make significant decisions as they employ AI solutions in their operations. For example, the use of autonomous drones is now governed by a global treaty. But before a drone’s rocket enters the operation, a professional should be involved in the decision-making process if it has the potential to kill someone with its rocket. This way, there is a disorganized set of laws and regulations that allow us to avoid some of the most serious control issues posed by artificial intelligence.
The issue is that artificial intelligence systems must increasingly make snap decisions. For instance, in high-frequency trading, algorithms now control more than 90% of all financial trades, meaning there’s little opportunity to
This also applies to driverless vehicles. The AI must be in charge of the situation since it must act quickly if a youngster runs out into the road. This raises intriguing moral dilemmas about control and AI.
Social and Cultural Impact
Chatbots and generative AI have a significant impact on the process of content creation for social media and other media channels. There are numerous types of content, like “deep fakes,” digitally produced images, and films that seem like real-life productions, which raise ethical issues. Further, to ensure privacy, safety, and diversity, ethical considerations are also important when it comes to face recognition technology.
The right solution here is to properly identify AI-generated content and make sure it is distinct from human-generated content. It begins by encouraging the creation of responsible AI content to avoid any harmful false perceptions.
Here, having advanced technologies that can help in recognizing deceptive content and deepfakes is another solution.
Unemployment
Automation has become a major issue in the AI and big data sectors. As the percentage of automation is going up in industries, people are taking on increasingly complicated responsibilities. These responsibilities are transitioning from physical labour, dominated in pre-industrial societies, to cognitive labour, which is characteristic of strategic and administrative employment in the global society.
Take an example of the trucking industry, which employs millions of people in India alone. What will happen to them if Tesla’s self-driving trucks enter the market in the next ten years? However, if we think about the decreasing number of accidents, self-driving trucks seem like an efficient option.
 Being Biased
Artificial intelligence systems need the right guidance and training, as well as additional precautions, to ensure that the data is free of bias.
For example, the number of white faces in the ImageNet database is much higher than that of non-white faces. If we train our system through an imbalanced database, our AI systems will to perform less effectively on non-white faces. It sometimes results in an unintentional bias that can be very significant.
The correct way to deal with one of these ethical issues in artificial intelligence is to eliminate as much prejudice as possible as we train our AI. It begins with an approach to recognizing the possibility of bias in our AI.
Unbalanced Power Supply
Today, big and massive corporations like Amazon, Facebook, and Google are taking advantage of the advances in artificial intelligence to destroy their rivals and become nearly unstoppable in the market. Further, there has been huge government funding for ambitious AI programs in nations like China. To define the importance of AI, Russian President Vladimir Putin states, “Whoever wins the race in AI will probably become the ruler of the world.
Unbalanced power supply can be the anwer when asked – what ethical concerns arise from the collection of data by large companies.
How do we ensure that the monopolies we’re creating are sharing wealth fairly and that some nations aren’t outpacing the rest of the globe in their pursuit of monopolies? In the field of AI, balancing such power is a significant difficulty.
Artificial Mistake
Whether it is a machine or a human, it is a fact that intelligence comes from learning. Generally, systems get the training during which they “learn” to recognize patterns and respond to user input. After the training, the system gets ready to move on to the testing phase, where it gets more examples and its performance is monitored.
We cannot deny that not every scenario a system can encounter in the actual world can be covered in the training phase. Sometimes, these systems get so tricked that people cannot use them. Random dot patterns, for instance, can cause a system to “see” objects that are not there. If we decide to depend on AI to usher in a new era of work, security, and efficiency, we need to make sure that the machine performs as planned. People can’t overpower it to use it for their own ends.
Lack of Transparency
Privacy is a primary concern when it comes to the ethical challenges of AI and big data. The use of data among companies, whether they are using personal information to smooth the overall operations or to boost the company’s profile, Here, transparency plays a key role; the degree of transparency is important to let users know how their information is used.
The use of AI and Big data has a negative impact in that it reduces the transparency in the operation. The technologies do not clearly show what data they are collecting and how they are going to use it.
Ownership-Related Confusion
With artificial intelligence, we can create amazing deceptive text, bots, and even deepfake films. But when the matter comes—who owns such content?—and in the event that this kind of bogus news circulates online, what should we do?
Apart from creating content, AI can also help you produce music and art. But again, the same question: Who gets ownership of a new piece of music composed by an AI? Who owns the intellectual property rights to it, and who might be entitled to compensation? The lack of ownership is among the top ethical issues in artificial intelligence.
Impact on the Environment
Sometimes we do not even consider the impact of AI on the environment. We generally assume that we are running advice engines on our website through the data that has been used to train an algorithm on a cloud computer. But our cloud infrastructure’s computer centers are energy-hungry. To understand it better, an AI training program can emit 17 times as much carbon dioxide in a year as the average American does.
How can we harness this energy for the greater good if it is harming our environment on this level? It is another major challenge of data that has made us think that if we should use artificial intelligence only for convenience, we may need to reevaluate our decisions.
Bottom Line
The moment you think of leveraging AI and big data for businesses, you find a number of ethical challenges posed by AI that affect society, decision-making, and privacy. While these challenges don’t lessen AI’s potential benefits or influence, they also can’t be disregarded when making policies that support the use of technology in a responsible way.
To effectively address these ethical challenges of AI and big data, companies need to collaborate interdisciplinarily and discuss with stakeholders. Further, they should have a comprehensive plan to maximise their benefits with minimum investment.
Frequently Asked Questions
01. What are the ethical challenges of artificial intelligence in business?
Some of the major ethical challenges of artificial intelligence include –
- Unjustified Actions
- Discrimination
- Being Biased
02. How do you overcome ethical AI challenges?
The most effective way to overcome ethical AI challenges is by establishing a code of ethics. It clearly outlines the values and systems your AI system should follow.
03. How AI is affecting small businesses?
AI is benefiting small businesses in numerous ways from enhancing operational efficiency by 80% to limiting their cost by 69%.
04. Is AI good or bad for business?
If AI is used smartly it can help a business enhance its overall by automating routine operations. Further, it allows the team to focus on more significance business aspects and finish pending tasks in less time.
