Society features

How has data science evolved to improve society?

AI thought leader Aruna Pattam discusses the broad applications of data science in society and the importance of diversity for the future of the industry.

Data science draws on a wide range of skills and expertise within the technological sphere. It uses statistics and mathematics, analytical modeling, machine learning and AI to analyze data and extract insights.

Shane O’Neill of Accenture said those who pursue a career in data science can potentially work in any industry that uses data, which is pretty much any industry today.

But even though we know data science is important, you might not know how many areas of society it touches and how it can be used to solve a wide range of real-world challenges.

To better understand the evolution of data science and its current uses, turned to Aruna Pattam. She is a global AI thought leader currently working as an AI and Data Science Manager at Indian IT multinational HCL Technologies for the Asia-Pacific and Middle East region.

“As a data scientist, I’m often the only woman in the room”

Pattam has spent over 20 years providing decision support platforms and systems using analytics, artificial intelligence and machine learning.

“My career started at SAS, where I learned how analytics could help solve business problems and create new opportunities,” she said.

“The more I learned about what this field had to offer, the more passionate I became about its potential, which led me down a path of exploring analytics to transform the financial services industry.”

The evolution of data science

While data science as a concept has been around for a long time, Pattam said the term itself didn’t really appear until 2008.

“In the beginning, it was statisticians who developed algorithms to perform analyzes using probability models or machine learning algorithms.”

From there, Pattam saw analytics used for operational purposes such as business intelligence and data warehousing. “Here, the analysis was focused on the speed of the algorithm, as opposed to its underlying statistical basis,” she said.

“Then I saw engineers working in transactional systems develop analytical models to guide the development of new features. Here, the analysis covered data management issues, as well as modeling and early implementations of machine learning algorithms.

After that came the full term data science, which incorporates a mix of everything that came before it, but with an emphasis on modeling and machine learning techniques.

Data science in the real world

Many people may know how data science is used for recommendation engines. They can also combine AI with facial recognition. But there are countless real-world applications that can help change industries and even society for the better.

For example, in healthcare, data science allows the processing of large amounts of clinical data to identify a set of treatments for a particular patient as well as likely side effects. A doctor can then use this information for personalized treatment of the patient.

“In the discovery of chemicals and drugs, [it can help] sort and compare various properties of millions of potential small molecules, synthesize, test and optimize in laboratory experiments before selecting the eventual drug candidate for clinical trials,” Pattam said.

“In the energy sector, patterns identified by data science help predict demand, improve performance, reduce costs, prevent system failures and thereby achieve greater efficiency.”

Data science can also help develop early warning systems in financial risk management, healthcare facilities, and agriculture. It can even help predict the effects of different climate crisis mitigation or pandemic management strategies and highlight the most promising ones.

Pattam said data science can also help “identify mental health issues at both population and individual levels and enable, for example, forum moderators to identify people in need quick action”.

Data science can be used to monitor and adjust operations in areas such as clean energy, logistics and communications, track and communicate health information to the public, and create smart cities that use services more efficiently. public spaces, better manage traffic and reduce climate impacts. .

However, while the benefits of data science in all of these areas are powerful, the industry is not without its challenges, especially when combined with AI and machine learning.

The challenges of the data world

Bias in AI is a huge topic and one that needs to be widely discussed as it could have serious consequences. There have been several examples in recent years highlighting ethical issues with AI, including an MIT image library to train AI that contained racist and misogynistic terms and the controversial credit score system in china.

Pattam said one of the most important ways to combat bias in AI is to do so “early in the development process” by including ethical considerations early on.

“Everyone has a role to play in making the use of AI ethical, unbiased and beneficial to all,” she added.

“We need to ensure that the use of data does not raise privacy concerns”

“Industry should be aware of its responsibilities when developing and implementing new technologies. The public sector has an influencer role as well as a facilitator that can support the unbiased implementation of AI systems through regulations and policies.

“Academia needs to develop industry-independent knowledge about the potential for bias in AI systems and share it with the public.”

In addition to concerns about bias, there is also a challenge with data privacy. “We need to make sure that the use of data does not raise privacy concerns,” Pattam said.

“The way to solve this problem is a whole-of-society response, where business and government play their part, but individuals also take responsibility for managing access to their information.”

Diversity in Data Science

Diversity is a key issue for all tech disciplines, and while the number of women in the field continues to grow, Pattam said women are still a minority in the data science industry.

“As a data scientist, I’m often the only woman in the room,” she said.

“Some of the reasons for this under-representation are that it is a male dominated field, sexism in STEM fields, prejudice against mothers, lack of female confidence, lack of female role models and lack of visibility, which makes it difficult for women to attain new opportunities.Women also face an uphill battle in terms of promotion and recognition within organizations.

Pattam said women starting out in data science should develop relationships, join a community, and seek out opportunities even when those seem impossible.

“Try to have a mentor, someone who will guide you through your career and give you advice on how to get where you want to be.”

She added that diversity in all respects, not just gender, is one of the biggest things she would like to see changed in the data science space.

“I hope more women get involved in data science because they have different perspectives that are often valuable. I also hope to see more people of color enter the field because there are big gaps here too. The world is not just white and male, so I think it’s important for everyone to play a role in changing the face of data science,” she said.

“I also hope to see more young people get involved. Children are the future and I believe they should have the opportunity to explore, learn and discover new things. I would like data science to become so openly available that it is included in all school curricula. »

Don’t miss out on the knowledge you need to succeed. Sign up for the brief dailySilicon Republic’s must-have science and technology news digest.