Big Data and Artificial Intelligence are revolutionising the way businesses operate, make decisions and engage with their customers. These technologies enable organisations to analyse vast amounts of data, uncover hidden patterns and make more informed and efficient decisions.
For impactful businesses, which seek not only to generate economic benefits but also to create a positive social and environmental impact, the use of Big Data and AI offers significant advantages. However, these companies must be particularly wary of biases in the AI algorithms and data they use, as they can perpetuate or even exacerbate the inequalities they seek to mitigate.
In this post, we will explore the advantages of using Big Data and AI in impact companies, the risks associated with biases, and why it is crucial for these companies to monitor and mitigate these biases.
Advantages of using AI and Big Data
Process optimisation and operational efficiency
One of the main advantages of using Big Data and AI is process optimisation and improved operational efficiency. Companies can use these technologies to analyse large volumes of data in real time and make faster and more accurate decisions. For example, in the manufacturing sector, sensors and IoT devices can collect data from machines, allowing them to predict and prevent failures before they occur, reducing downtime and maintenance costs. This in turn allows tracking and traceability to be demonstrated, providing greater transparency into the product manufacturing process.
Personalisation of products and services
Big Data and AI also allow companies to personalise their products and services to better meet the needs of their customers. By analysing consumer behavioural data, companies can identify individual patterns and preferences, allowing them to offer personalised recommendations and targeted promotions.
Having a detailed understanding of the issues to be targeted rather than using general knowledge allows impact businesses to design their strategies with greater precision and then to more realistically evaluate the results achieved.
By analysing data from a variety of sources, enterprises can identify market opportunities and emerging trends. This allows them to develop products and services that meet changing consumer needs. For example, companies such as ECOALF are using sustainability data to develop fashion products made from recycled materials, responding to the growing demand for eco-friendly products.
Improved decision making
Advanced data analysis enables companies to make more informed decisions. AI tools can process large amounts of data and provide insights that would be impossible to obtain manually. For example, in the financial sector, companies can use AI to analyse historical data and predict future trends, enabling them to manage risks more effectively and make better investment decisions.
It is common to find large historical data sets on social and environmental issues, so being able to model and look for patterns and design impact strategies on these issues is now much more effective.
Human resource management
These technologies can analyse employee data to identify patterns in retention and performance, enabling companies to implement more effective strategies to attract and retain talent. In addition, AI can assist in the hiring process by analysing resumes and predicting which candidates are most likely to succeed in the company.
Cost reduction
By optimising operational processes, predicting failures and improving efficiency, companies can reduce their operating expenses. In addition, data analysis can help identify areas where costs can be reduced without compromising product or service quality.
The speed at which these analytics can be available in near real-time is significant. However, it is important not to get caught up in the fever of continuous improvement. We must not forget that changes take time to be internalised by people. It will be the machine that shows us the way, but it will be the workers who have to walk it.
Biases in the use of AI and Big Data
Biases in AI and Big Data can arise from a variety of sources. The data used to train AI algorithms can be biased if it is not representative of the general population or if it reflects historical bias. For example, if a hiring algorithm is trained on data from current employees of a company that has a history of gender discrimination, the algorithm may perpetuate these biases by preferring male candidates.
Impact of biases
Biases in AI algorithms can have serious consequences. They can perpetuate injustices and inequalities, negatively affecting marginalised groups. In criminal justice, for example, crime prediction algorithms have been criticised for biasing against racial minorities, resulting in increased scrutiny and surveillance of these groups. In the financial sector, credit algorithms can discriminate against individuals based on factors such as their postcode, which may be correlated with race or socioeconomic status.
In Chicago, the use of an algorithm to predict crime produced a false positive. The reason is that if more police are deployed in an area, it is logical that more arrests will be made, but it is not because there is more crime than in other areas.
The facial recognition system used by some police forces has shown significantly higher error rates for people of colour. Another example is the use of risk assessment algorithms in the US criminal justice system, which have been criticised for incorrectly predicting a higher risk of recidivism for African-American individuals compared to their white counterparts.
Detecting and mitigating bias
Detecting and mitigating bias in AI and Big Data is crucial to ensure ethical and fair use of these technologies. This involves a proactive approach to data management and algorithm development. Companies should implement data governance policies that ensure that the data used is representative and of high quality. In addition, they should conduct regular audits of their algorithms to detect potential biases and adjust them accordingly. The use of explainability techniques in AI, which allow understanding how and why an algorithm makes certain decisions, can also help to identify and correct biases.
Transparency remains crucial in the technology world.
Importance for impact companies of using AI and Big Data and the need to monitor biases
For impact companies, the use of Big Data and AI is not only about operational efficiency or increasing profits, but also about creating social and environmental value. These companies seek to solve social and environmental problems through their business activities. Big Data and AI can provide the necessary tools to identify and address these problems effectively.
Transparency and Trust
Transparency in the use of Big Data and AI is crucial to maintaining public and stakeholder trust. Impact companies must be clear about how they collect, handle and use data. This includes being transparent about the measures they take to avoid bias and ensure their algorithms are fair and equitable. Trust is a key factor in the long-term success of these companies, especially when their mission is focused on creating a positive impact on society.
Given that these companies often work with vulnerable populations, it is especially important that they ensure that their technologies do not discriminate or exclude these groups. This involves not only detecting and correcting bias, but also implementing inclusive practices in data collection and algorithm design.
If technology must always be a tool at the service of people, here one could even say that it is a tool for the care of people. The level of demand must be much higher in all parameters. Some of the measures to be taken into account are:
– To ensure that your development teams are diverse and inclusive, as this can help identify and address biases that might otherwise be overlooked.
– Use diverse and representative datasets to train your algorithms.
– Implement explainability techniques and conduct regular audits of your algorithms to detect and correct biases.
The long-term impact of responsible use of Big Data and AI by impact companies cannot be underestimated. These technologies have the potential to transform the way we address social and environmental problems, but only if they are used ethically and fairly. By prioritising equity and inclusion in their data practices, impact companies can not only achieve their business goals, but also contribute significantly to the well-being of society and the environment.
In this way they can influence the companies that develop these same technologies. Users of their services and products will be able to point out the failures and negative impacts they produce. Let us not forget that in addition to the biases we are talking about here, the environmental impact of these technologies is also an important issue.
Conclusión
The use of Big Data and AI offers numerous advantages for impact companies, from process optimisation and product customisation to improved decision-making and innovation. However, these companies must be especially wary of biases in the data and algorithms they use. Perpetuating biases and inequalities can undermine their mission and damage their reputation. It is therefore crucial that impact companies implement robust data governance practices, ensure transparency, and take proactive steps to detect and mitigate bias. In doing so, they will not only be able to maximise the benefits of Big Data and AI, but also contribute to a more just and sustainable future.
