AI has applications in almost every single industry, including healthcare. 

How about the privacy of our own data?

Artificial intelligence (AI) has undergone a period of intense progress and consolidation of several key technologies. AI has the potential to revolutionize medicine, as can be seen in medical imaging applications, where computer visualization techniques, traditional machine learning 12, and deep neural networks have achieved remarkable success. This progress can be attributed to the release of a large, curated body of images on ImageNet. It is known to produce high-performance, pre-trained algorithms that facilitate learning transfer, leading to increased publications in oncological applications. Even, Tumor detection, genomic characterization, tumor grade prediction, outcome risk assessment, relapse risk quantification, and non-oncological applications such as chest X-rays and retinal fundus imaging. Claims have led in recent decades to a number of evolving techniques that allow data to be manipulated to protect Personally Identifiable Information (PII). In contrast, the data remains valid for analysis.

Their complexity continues to increase, and the handling of data protection analyses goes beyond the scope of this article. The ability to train deep learning systems (DL) on large data sets increases the speed of analysis, requiring more and more data and increasing the risk of a lack of privacy. Other sectors such as finance, life science, and government have similar needs to protect the PII information shared by internal organizations, businesses, and governments.

Federated Learning trains algorithms on decentralized edge devices that exchange data samples. Still, it is difficult to verify and is at the mercy of fluctuations in the performance and calculation of the Internet.

For trust to be necessary, not only transparency must be paramount, but also privacy. Homomorphic encryption is a form of encryption that allows calculations to encrypt data, but it is slow and demanding. Differential privacy discloses information about a record but withholds information about individuals and suffers from errors inaccuracies caused by injected noise.

https://blog.tensorflow.org/2019/03/introducing-tensorflow-privacy-learning.html

This system is intended to help manage data protection in machine learning applications. The international framework for the EU General Data Protection Regulation (GDPR) aims to give consumers more control over the collection and use of personal data. The EU General Data Protection Regulation page of IAPPs collects guidelines, analyses, tools, and resources you need to ensure you comply with your obligations.

Federated Learning is a new approach to machine learning that decentralizes the training process and allows users to preserve their privacy by not transmitting their data to a central server. For example, Gboard uses federated machine learning to train search, query, and prediction models on users’ mobile phones without sending individual queries to Google. It increases the efficiency of the process across many devices.

Private federated learning is a process that allows each node in the learning model to hide its data. As an open-source TensorFlow library, TensorFlows privacy works on the principle of differentiated privacy.

I decided to look at what Privacy International has to say about artificial intelligence. Privacy International is a charity that challenges governments and businesses that want to know everything about individuals, groups, and society as a whole. Governments and businesses can no longer use technology to monitor, track, analyze, profile, manipulate, and control us. The future we want is one in which people control their data and the technologies they use.

Different national, institutional, economic, political, and cultural conditions influence how AI affects convenience, efficiency, personalization, privacy, and citizen surveillance. Recognizing the enormous benefits of artificial intelligence and mitigating undesirable effects will require enlightened responses from many stakeholders.

Should we stress the importance and urgency of engaging multidisciplinary teams of researchers, practitioners, policymakers, and citizens to develop and evaluate algorithmic decision-making processes in the real world that maximize fairness, accountability, transparency, and respect for privacy?

The widespread availability of human behavioral data and the increasing capabilities of artificial intelligence (AI) enable researchers, businesses, practitioners, and governments to use machine learning to address critical societal problems (Gillespie, 2014; Willson, 2017). Notable examples include the use of algorithms to estimate and monitor socio-economic conditions (e.g.. Eagle and al., 2010; Soto et al., 2011; Blumenstock et al. 2015; Venerandi et al. 2017; Beings and Hillebrand et al. [2020]) and to map the spread of infectious diseases.

I have more questions than answers!