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Why research and development is so important for economic growth, according to the IMF

There are three measures that companies may take to eliminate bias in AI systems.

Artificial Intelligence

Is this scenario sounding familiar to you: engaging with smart devices that don’t listen to commands? People may be taken aback by this failure, as though their intelligence isn’t on par with that of the robots. While selective interaction is not the goal of AI development, such occurrences are expected to occur more frequently among “minorities” in the IT sector.

The worldwide artificial intelligence (AI) software industry is expected to grow rapidly in the future years, with a projected value of about 126 billion dollars by 2025. Because of the development of AI technology, many current businesses are being forced to change their business models and transition to AI. However, as technology advances, there is growing concern about biases in algorithm creation for all of these tools.

How AI flaws are discovered

Algorithmic bias isn’t a new concept. Engineers, on the other hand, have spent more time building AI algorithms to tackle difficult problems than monitoring and reporting the possible hazards that these technical advancements may cause. With the advent of discriminatory behaviors, we have already witnessed examples of technological failures. For example, Microsoft’s self-learning chatbot Tay was introduced on Twitter in 2016.

The AI tool may pick up basic language skills and eventually engage in a conversation on its own. On social media, however, the bot developed racist and sexist characteristics. Another example occurred at MIT, where Joy Buolamwini did a biased experiment without realizing it while working on face recognition. She was not recognized by the AI as accurately as her white buddy since she was a dark-skinned woman. The results were entirely irrelevant to the experience, and she discovered that the computer correctly recognized 99 percent of white women but only 65 percent of black women were.

Was the AI’s behavior motivated by human intent? Perhaps, but perhaps not. These instances do not imply that the AI tools were meant to be racist or that they were fundamentally defective. Regardless, their design was skewed, and they were not sufficiently regulated before going public. Data biases can lead to biased actions that are either intentional or unintentional, continuing generations of bias (es). Worse, because the prejudice that results is nearly usually an accidental emergent characteristic of the algorithm’s use rather than a conscious choice by its designers, identifying the root of the problem and explaining it to a court is difficult. Machines have a tendency to offer the – misleading – image of neutrality.

In an undeniably biased and imbalanced world, how can an ethical and non-biased AI application be developed? Is AI capable of becoming the Holy Grail by fostering more balanced communities that eliminate conventional inequity and exclusion? It’s too early to say, but it appears that many trial-and-error periods will be required before we reach an agreement on what and how AI may be utilized responsibly in our communities. Comparable to institutional racism, which necessitates fundamental changes in the whole ecosystem, AI development issues need a similar transformation in order to produce better results. To address this problem, we suggest putting humans at the forefront of technology progress by focusing on three areas.

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1. Human beings who are not biased (biased).

Developers and particular individuals in authority are behind the creation and execution of algorithms. The developer’s professional environment is far from diverse today, as seen by the statistics, which illustrates some of the thinking logics that create prejudices. Increasing the diversity of and access to developer jobs in the industry’s main players would provide a more critical viewpoint on how algorithms are produced.

Rather than decreasing human inclusion, this would boost it. Assume that algorithmic bias is defined as the imposition of particular beliefs using computers and math as an excuse. In such situation, we might call into question the institutional logic that allows bias and discrimination to persist.

To guarantee that human bias does not pervade the invention and growth of algorithms, more control, monitoring mechanisms, regulation, and shared ethical frameworks are required. We agree with Georgia Tech professors Ayanna Howard and Charles Isbell that acknowledging the importance of diversity in data and leadership, as well as demanding accountability in certain decisions, are critical guiding principles for achieving a more just development and implementation of AI in the future.

2. Rather than data for prejudice, data for good

A researcher at the University of Ontario utilized the MNIST dataset and reduced the database of 60K pictures down to only 5 to train an AI model, which is an important endeavor that might help eliminate historical dataset biases. If these techniques can be effectively applied to a variety of situations, AI will become more accessible to businesses who cannot afford large datasets. It will also enhance data privacy and gathering because it will require less information from individuals to train appropriate algorithms.

3. Informing individuals about the benefits and drawbacks of AI applications

AI development offers a number of significant difficulties in terms of gaining a better knowledge of society, politics, economics, and even our everyday lives as citizens. As artificial intelligence (AI) becomes more prevalent in corporate operations, influencing people’s choices and opportunities, more education is required to promote awareness and comprehension of these issues.

Citizens’ technological preparedness will boost AI acceptance and have a beneficial impact on critical evaluations of AI deployment and impacts. A better informed populace will be less tolerant of manipulation and accept biased or unfair AI applications, such as monitoring, which may infringe on civil freedoms and rights.

Making machines more human, or even limiting human intellect, has long been regarded as one of technological progress’s ultimate aims. Human-centered technology development means that machine developers and corporations should not just strive for innovation, but also consider the machines’ potential social effect. Humans are imperfect, which means that our society is inherently filled with systemic and institutional prejudices that we aren’t necessarily conscious of. However, we should avoid reproducing the same problems in the machines we create.