AI News, BOOK REVIEW: Anonymity and Levels of Support on a Health artificial intelligence

Manage anonymous read access to containers and blobs

You can enable anonymous, public read access to a container and its blobs in Azure Blob storage.

When you grant public access to a container, then anonymous users can read blobs within a publicly accessible container without authorizing the request.

You can configure a container with the following permissions: From the Azure portal, you can update the public access level for one or more containers: The following screenshot shows how to change the public access level for the selected containers.

To set permissions for a container using the Azure Storage client library for .NET, first retrieve the container's existing permissions by calling one of the following methods: Next, set the PublicAccess property on the BlobContainerPermissions object that is returned by the GetPermissions method.

Finally, call one of the following methods to update the container's permissions: The following example sets the container's permissions to full public read access.

Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers?

The potential of artificial intelligence (AI) to promote better health care has taken the centre stage in modern debates on public health and health policy.

AI research began in the 1950s, when Alan Turing raised the idea that machines could 1 day think as humans.1 Then came, in 1959, the first instance of ‘machine learning’ (ML), where computer scientists created a program capable of solving puzzles on its own.2 Now, AI promises to lead the next major technological revolution, similar in stature to electricity and the internet.3 In the field of health care, AI has already led to improvements, particularly in areas such as precision medicine, diagnosis tools, psychological support, and help for the elderly.4 AI technologies generally require large amounts of both personal and non-personal data to function.

In health care specifically, AI technologies rely on personal information, including health-related data extracted from medical files or research participants’ results.5 Promoting AI and capturing its benefits for the health care system yet depend, in large part, on procuring a convenient access to this sensitive data.6 Ensuring that privacy protections are in place appears essential, especially with individuals showing substantial concerns about sharing their data in the medical and clinical context.7 Suggestions to implement public open databases to promote medical research have created some controversy in Europe and in North America.

Although widely supported by health care professionals, the failure of the project was largely due to a lack of transparency about the envisioned uses of health information and the possibility to opt-out.8 In the United States (US), studies have shown that individuals’ willingness to participate in research involving their genetic data is affected by their concerns about their ability to protect their privacy in such context.9 Paradoxically, this lack of trust is counterbalanced by a growing popularity of direct-to-consumer genetic testing and health monitoring devices.

If these laws are found to provide insufficient protection, the result could be a decrease in data flow from the EU to North America, due to the need to proceed via the adoption of additional contractual clauses.11 Such decrease would not only negatively affect research and development of AI technologies in both countries but would also interfere with any attempt at cooperation in the field.12 In this context, it appears all the more pressing to consider appropriate measures to consolidate privacy protection and promote stakeholders’ trust.

by artificial means, specifically by computer’.13 In many health care systems, AI has already been successfully deployed, mainly in the form of ML- and deep learning (DL)-based technologies.14 In both ML and DL, a certain amount of data (the input) is provided to the system for processing (through one or several algorithms), in order to provide an output.

Some authors refer to this disturbing lack of transparency in DL as a ‘black box’ phenomenon,16 especially problematic when dealing with patients’ sensitive data.17 AI research applied to health care is a rapidly growing field.18 As of today, AI in health care is generally concentrated around three areas: oncology, neurology, and cardiology.19 These are all areas of medicine in which early detection is crucial.20 In clinical care, AI ML and DL systems are already assisting physicians in decision-making, providing them with relevant and up-to-date information for diagnosis and treatments.

The use of AI, combined with imaging, has shown great potential in supporting the rapid identification of the presence or absence of certain types of cancer, sometimes with greater accuracy than specialists.21 DL systems have also shown great potential in promoting the development of precision medicine,22 using improved prognostic and diagnostic models.23 Electronic Health Records and telemedicine are also becoming widespread, showing significant capacity to shorten the time spent by health care professionals in addressing specific tasks.24 The use of carebots and other similar robots is growing in countries with acute aging populations.25 AI in health care is not limited to assisting in clinical care or decision-making.

By automatically spotting similarities in patients’ medical records, AI systems can support researchers in quickly identifying the optimal patient cohort for a specific clinical trial.26 The ability of AI systems to make predictions based on larger sets of data can also benefit public health.

AI based on Big Data has contributed to the development of ‘Precision Public Health’ to help predict and understand public health risks and customize treatments for definite and homogeneous subpopulations.27 In order to maximize these possibilities, considerable suggestions to transform health care systems by opening access to data collected in both clinical trials and medical care have garnered attention.28 The paradigm of a ‘Learning Health care System’ (LHS), for instance, describes a health care system ‘in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the health care delivery process and leads to continual improvement in care’.29 A LHS is thus based on the integration of research and practice as a way of facilitating data and knowledge transfers by improving access to medical data (collected in electronic health files for example).

These norms were built around the belief that research and clinical care need to be clearly delineated to protect patients and research participants.30 The LHS paradigm requires novel, more appropriate, ethical and regulatory frameworks31 which should integrate privacy and data protection mechanisms, as those are a crucial vector of success of this enterprise.

One obvious example was given by the illegal use of personal data compiled by Facebook to support the 2016 American presidential campaign.38 Such events, highly covered in the media, have drawn the attention of the public to the risk of personal data usage for commercial or political purposes without proper consent.

This regulation aims at strengthening the free circulation of non-personal data and facilitating the development of a common digital market within the EU.40 Generally, personal data are data that allows the direct or indirect (eg through triangulation) identification of a data subject.41 Canadian federal law defines personal information as ‘information about an identifiable individual’ (art.

Such discretion could, in our view, hinder this objective and has been criticized for its damaging impact on international harmonization initiatives.43 Interestingly, in the US, a federal regulation also covers Protected Health Information (PHI) collected by defined entities as a specific type of data.44 The Health Insurance Portability and Accountability Act (HIPAA)45 provides with specific standards for the collection and the use of PHI since 1996.

HIPAA only applies to data processed by ‘covered entities’ and ‘business associates’, meaning that data miners are typically excluded from its application.46 Technology giants like Google, Amazon, which deal daily with large amounts of personal data including health-related ones, are generally not covered by the regulation.47 Moreover, HIPAA’s ‘Privacy Rule’ only protects identifiable information (part 160 &

Under HIPAA, de-identification can be completed in two ways: either by removing specific identifiers enumerated in the law from the data set or by having a statistical expert confirm that the risk of re-identification linked to a specific data set is sufficiently small (45 CFR § 164.514(b)(1), HIPAA).

To ascertain whether means are reasonably likely to be used to identify the natural person, account should be taken of all objective factors, such as the costs of and the amount of time required for identification, taking into consideration the available technology at the time of the processing and technological developments.’ The categorization of sensitive data presents advantages, as it accounts for the need for additional caution when dealing with health-related data.

In an op-ed following the revelations of illicit uses by Facebook of personal data for political purposes, the Privacy Commissioner of Canada warned against the limitations in Canada’s legislation on privacy and urged for the revision of PIPEDA.53 In line with the recommendations formulated by the Privacy Commissioner, the Committee on Access to Information, Privacy and Ethics of the House of Commons published a report with comparable reform propositions for PIPEDA.

The Committee emphasizes on the necessity to adapt Canada’s legislation in order to maintain adequacy status with European regulations and prevent a potential chilling effect on commercial exchanges with the EU.54 Meanwhile, the Council of Canadian Academies concluded that Canada’s current legal framework on data regulation is not only unsuccessful in protecting privacy but also gravely hinders timely access to data for health research.

The reporters recalled the dilemma policymakers are faced with and the growing need for amendments to solve it: 55 ‘The primary, overarching challenge in Canada, as in other jurisdictions, is to meet two fundamental goals at the same time: to enable access to health and health-related data for research that is in the public interest, on the one hand, and to respect Canadians’ privacy and maintain confidentiality of their information when it is used for research, on the other.’ In order to simultaneously better protect privacy, prevent unconsented uses of data and favor research through data sharing, both the Canadian and the American frameworks are faced with the need for substantial revisions.

Allowing the use of the same dataset for several purposes is thought as beneficial, if not crucial, to most scientific development.57 Recital 156 specifies however that: ‘The further processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes is to be carried out when the controller has assessed the feasibility to fulfill those purposes by processing data which do not permit or no longer permit the identification of data subjects, provided that appropriate safeguards exist’.

Making broad consent the rule and specific consent the exception in scientific and health care research could be an interesting alternative, especially in fields like biobanking, where the re-usability of biological samples and data are central.61 Consent requirements should be reinforced in the American and in the Canadian frameworks, but in research, and especially in health care, broad consent seems well suited to the biobanking context and potential AI technologies based on the data gathered.

This right aims at enabling individuals to take better control over their data and encourage competitiveness.62 In health care, data portability is thought to have practical benefits for data access, as it enables users of fitness trackers, for example, to save years of data compiled on an app and share it with their physician or with a research endeavor they wish to support.63 Yet, Article 20 excludes personal data which has not been provided by the data subject herself from its scope of application.

The data resulting from the analysis made of the data collected by a health wearable device, for example, is thus not covered by this right, despite its potential benefit further research.64 Under HIPAA, individuals have a comparable right to get their information transferred from one health service provider to another.

A health record transferred to a non-covered entity may then, at best, fall into the scope of application of another privacy regulation, but could also be completely passed over by US data regulation.65 While data portability can be beneficial for research by facilitating data transfers, in the US context, it also reduces HIPAA’s already limited scope of application.

Given its potential to favor data transfers in research (by facilitating data transfer from any file to a research project in which a data subject wishes to participate, for example), the Committee on Access to Information, Privacy and Ethics recommended that PIPEDA be amended ‘to provide for a right to data portability’.66 The right to data portability should be seen as a quintessential part of any privacy regulation at the time of AI, as it empowers individuals to better control the use of their data by re-directing it where it is most useful.

In cases where the right to erasure does apply, its implementation will be hindered by the technical difficulty to ensure complete and systematic deletion of personal information, especially when already shared with collaborators.67 In the context of AI, one specificity of DL is that the algorithm used to obtain an output is automatically created based on data that has been previously introduced.

Accordingly, the article 29 Working Party issued Guidelines to help determine when a DPIA is required.69 Among other criteria, a processing should be considered ‘likely to result in high risk when it involves sensitive data, data concerning vulnerable subjects (eg patients), or when it is based a new technology or an innovative use of an existing one.

An exception to the requirement to perform a DPIA can be found at recital 91, which specifically provides that: ‘the processing of personal data should not be considered to be on a large scale if the processing concerns personal data from patients or clients by an individual physician, other health care professional or lawyer.

Following the Facebook data breaches, the opportunity to provide Commissioner with more extended investigation and sanction power may warrant further consideration.79 If sanctions under PIPEDA may be insufficient to ensure compliance from industrial giants, the US legislation does not provide a satisfying harmonized framework on the territory, making high sanctions difficult to apply at times.

A general loss of trust from the public is understandable given the high-profile examples of misuses of personal data such as revealed by the Cambridge Analytica case.80 The GDPR’s new mechanisms aiming to prevent such unwanted uses of personal data, especially through the prohibition of opt-out scenarios and some consent requirements, could guide North American policymakers.

The specificities of AI and the novel risks brought for privacy protection are, at least partially, addressed in the GDPR: although some mechanisms seem restrictive and could be problematic for AI developers (such as the right to erasure), the general effort toward increased responsibility of data actors must be acknowledge and should inspire the adoption of more protective regulations.

Finally, we believe that developing a compatible international framework to protect personal information that enables responsible data sharing and cross-border data transfers would be beneficial to all parties.81 Let us also not forget that, ultimately, the benefits we can expect of AI are directly determined by the sum of data such technology is allowed to use and be trained on.

Chatbot Consultancy work Artificial Intelligence, QnA, LUIS

am looking for someone to initially do a very short piece of consultancy work for my client based in Somerset to support some work they are doing on an internal facing chatbot for staff to get anonymous information, advice and support regarding their own wellbeing both inside and outside of the workplace in a discreet and efficient way.

Based on a number of factors including costs, data hosting locations, speed of development, specialist skills required etc, they have decided that the best approach is to develop a QnA bot within the client's Microsoft Azure Tenancy, maximising their existing technology investment.

The queries will be sent to the bot service, held in the secure cloud environment, and, using a set of semi-structured content such as FAQs, documents and web locations, answers can be given to the user.

As a brief summary, the data would sit on a SQL server in the client's cloud environment but data can be added via the QnA maker which would be maintained by ICT and an ICT admin will then be able to add additional data at a customer's request.

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