With the growing sophistication of technologies like Artificial Intelligence, Machine Learning and the usage of Data Science algorithms, the process of computational decision-making is getting more automated with emphasis on the use of a trained system to generate valuable outcomes.
Considering how the mechanism of any computer-based decision-making process takes the order, there can be a possibility of bias in the output of such systems, which may become a cause for bias in the real world.
Algorithmic-bias is the systematic and repeatable errors in the computational outcomes that create a lack of fairness in the process. In today’s scenario, algorithms are ubiquitous and play a dominant role in our daily life. Many of our decisions get influenced by algorithms encompassing digital systems, making algorithmic-bias a critical issue to ponder on. The algorithms often get trained on data sets, and very often, these data sets aren’t even adequately labeled. An algorithm will become better at a task if it gets trained with more data. But this training data is often produced with less accuracy, creating a primary base for the bias to take form.
It is important to define the ethics of the usage of advanced technologies such as Machine Learning and Artificial Intelligence in the usage of various processes and implementations as in the Internet of Things, so that a holistic growth may cover large parts of the society. The development of algorithms needs to consider diversity, starting from the development team and the input data.
There may be cases when the effect of any bias may bring a minimal change in the overall process and output. Still, the aspect of user quantity may distort the overall efficiency of the diversity-by-design principle. An independent review of the said facet may further facilitate the effectiveness of the comprehensive system. A formal audit of the algorithms by any independent entity may check for the bias both from the view of input data and output decision.
With the continued emphasis on digital transformation, facilitated by the ongoing pandemic, the adoption of digital processes is very likely to see high growth.
According to Gartner, by 2024, 75% of large enterprises will be using at least four low-code development tools for both IT application development and citizen development initiatives.
TinyML is the ML applied on edge/local IoT devices (like on a fitness band), instead… Read More