Developing technology ethically: who is responsible?
With the rapid advancement in artificial intelligence (AI) and machine learning (ML) capabilities, methods of data gathering, and the associated use cases, have become much more sophisticated for businesses in recent years. This has led in turn to increased concerns around how this data can be governed and used in an ethical way. Ethical management of data is increasingly at the forefront of developers’ considerations when building new applications.
In fact, research by found that 82% of developers say ethical concerns are now much more of a consideration than ever before. The COVID-19 pandemic has undoubtedly heightened these perceptions given there is now an increased requirement for people to share sensitive information, such as for Track and Trace purposes with apps collecting customer data in restaurants or gyms. As the use cases multiply, a question persists around who exactly should take responsibility for the ethical aspects of application developments, and what role the developer plays.
The current situation
Organisations are currently split in their ethical approach towards development, as report ethical issues to the legal or HR team, 23% find it’s sorted at C-level and only 19% actually have a dedicated ethics officer. Surprisingly, the actual responsibility for dealing with ethical issues lies with the developer within 16% of organisations. There isn’t a consistent approach across the board, and within many organisations the approach and responsibility is not clear at all.
Due to this confusion around responsibility, and the variety of different roles across organisations that are incorporating the management of ethical issues, it’s no surprise 13% of developers don’t know who to report these concerns to. There is an issue around lack of awareness of ethical issues, but there’s also a major issue around accountability – something that needs to be rectified when sensitive data is at play.
The ethical challenges developers face are likely to only increase in the coming years, whether in relation to cybersecurity, the use of data or the growing use of technologies like AI and ML. With more and more issues relating to the use and treatment of data, organisations need to decide who is responsible for the ethical considerations around data and its usage.
Solutions to the ethical question
In creating clearly defined roles within an organisation, such as a data protection or chief ethics officer, businesses can send a strong message to employees and customers that trust, and by extension, privacy, security, and ethics, are at the forefront of the culture of an organisation.
For developers, having a clear picture of who they should talk to about ethical concerns and the use of data will help them ensure that the applications they are building comply with best practices. Increasingly, more developers (52%) are looking to consult those responsible for ethical considerations as they build applications, both to achieve compliance with regulations today, and to establish technology and processes that ensure ethical data handling including compliance going forward.
Creating strong accountability and processes and removing the onus for these issues from developers, will help with their workload and their stress levels. Currently more than eight out of ten developers feel that they work in a pressured environment. The solution to this can include the use of intelligent data platforms with integrated frameworks to ensure that developers build every application in accordance with regulations.
This kind of solution is particularly crucial for financial and healthcare institutions where personal data is stored in large volumes. The use of technology makes it easier for ethical data handling to be built into applications from the outset and reduces the number of people and processes that need to be involved.
The wider considerations of ethical technology
More than ever, consumers are now choosing the organisations they interact with based on their ethical efforts and no longer just economical or convenience factors. With this shift in consumer demand, how an organisation protects, processes and safeguards personal data is increasingly becoming a competitive differentiator, so businesses must make this a priority when developing new technologies.
As part of this, businesses must continually come back to the idea of what should they do with data, rather than what could they do with it. This mindset should be adopted across the organisation. There should also be individuals whose clear duty is to govern the use of data and ensure that these morals are upheld. By always considering the ethical standpoint, an organisation can ensure compliance to regulations, enhance its brand, reduce stress on its employees, and also ensure the loyalty of its customers.
By Jeff Fried, Director of Product Management, InterSystems
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.