Big data and advanced techniques of Data Science machine learning are improving the businesses sphere and making the decisions that were unattainable before. However, as the field expands, issues of ethics importance have also emerged as important. As with other data-driven technologies, it is incumbent upon the society and its institutions to ensure that environments surrounding and supporting the technologies are used appropriately, and that such environments are freeing, fair and efficient without violating the letter or the spirit of the law. Namely, this article of the project focuses on reviewing the primary concerns regarding Ethics in Data Scientists and Machine Learning and gives the suggestions to resolve these problems pertaining to Artificial Intelligence.
The Problems with Big Data: Why Ethics Matter in Data Science and Machine Learning
Data is now a strong asset and at the same time a valuable resource when it comes to organization security. Businesses gather and process lots of data and, based on it, make decision impacting millions of individuals. Yet, auto-driving, which many key processes use machine learning models, can act biasing or causing harm if its models are not well designed. Ethical data science not only preserve individuals’ rights to privacy but also be instrumental in preserving companies ‘reputational capital besides the fact that it will help them avoid running foul of the law.
This blog aims at identifying and discussing some of the most important ethical questions that concern data science and machine learning.
Data Privacy and Consent
Ethical data science cannot be complete without observing the privacy of the individuals whose data we obtain and use. As the collection of personal data grows, there is a tendency of privacy violations. Getting data responsibly entails having to make sure that the users agree on how their information will be used.
Best Practices:
New policies for the use of big data must have openness in the gathering and analysis of information.
keep the customers person information safe by using data anonymization techniques.
Other regulations regarding the protection of data include GDPR, CCPA and other comparable acts.
Bias and Fairness
Machine learning models trained on data that can contain societal bias reproduce that bias within the output. To the lack of these it can contribute to reinforcement or even increase of inequality by means of t
Transparency is the first step towards explainability.
As the models and complexity increases, they are termed as black boxes and the nature of decision making is not very clear. This tug of opaqueness denounces accountability, especially when the decisions made will directly affect society, for instance, in hospitals or in the financial sector.
Best Practices:
There is nothing that prevents one from making models more understandable by employing some of the XAI techniques.
Inform users on how decisions are made.
Structure and justify instances of document model design and decision.
Uncertainty, Risk and Responsibility: The Problem of Accountability in Automated Decisions
Robotic systems are today making decisions on their own with a minimal or no input from the human operators. However, who or what is to blame if the decisions having such impacts are made in the first place? In ethical data science, it is still incumbent upon companies to develop accountability mechanisms that answer this question.
Best Practices:
Distribute ownership over data science departments and executives.
Make sure systems have an automatic shut off or place where a human can have to step in and take control.
It is important to perform the evaluation of ethical policies frequently and to integrate the results into account given the progressive advancement of technology.
Data Security
Risk management of data is not only an ethical and legal imperative, but it also makes commercial sense. Security violations and intrusions are detrimental steps that can damage user information and erode confidence in the system. Use by ethical data scientists should respect the customer information input process through incorporating unfaltering security procedures.
Best Practices:
Use encryption and cyber security as key components for any product that will be developed.
Employ several factor authentication in an attempt to protect vital data.
has to perform routine security audit to know where it has failed.
Resources in the context of sustainability and resource use
The models especially the deep learning models demands large amounts of computational resources hence high energy consumption and carbon footprint. In decision making processes concerning data science, an important ethical consideration should be the effects of these technologies on the environment.
Best Practices:
Minimize the dependency of models on computational resources.
Choose environment friendly cloud solutions strategies.
Where you can, select energy-beneficial algorithms and equipment.
For instance, an ML model trained from biased data set for hiring a workforce is likely to provide job opportunities to certain groups of people and deny others.
The Consent in Data Collection and Transparency
In many cases, data scientists require to gather as much data as possible for assessment and evaluation. Nevertheless, ethical collection cpmpels the manifest consent of users and their understanding of the intended use in collection of their data.
Best Practices:
There should be transparency in explaining or narrating all details about the retrieval of data.
Users should also be allowed to withdraw a consent that has been given by them.
Do not employ data for something they never consented to.
Ethical Dilemmas and Recommendations for Data Science as Applied to Machine Learning
To foster responsible data science, one must include ethics in every pint throughout the Machine Learning process starting with data collection, training, until deployment. Here are some additional strategies to foster ethical practices:
It helps to predict the behavior of data scientists accurately and allows following a clear code of ethics. Organisations may well use the various frameworks available, like IEEE Ethical Design for AI or the EU Guidelines on Ethics for Trustworthy AI.
Ethics Review Checklist
Just as software is sometimes put through a quality assurance process, data science projects need an ethical check. An ethic committee or other selected team of workers can assess and suggest methods to avoid possible harm.
That’s why, one should take efforts to increase the diversity of data science teams.
It is given that organisations which have a composition of diverse employees are better placed identifying bias involved in data and models. Friedman offers the opinion that blended teams are beneficial in ensuring possible ethical problems that are not identifiable in homogenized groups will be addressed.
Spending for training in ethical data science
Most ethical questions result from ignorance or unawareness. For responsible data science to be achieved, there is the need to work on the ethics to support it by offering training to data scientists, engineers as well as decision makers.
