May 18, 2021

Study shows how biases could be maintained by AI devices

AI
Technology
Data
healthcare
2 min
The use AI in healthcare can benefit everyone, but if left unchecked, the technologies could unintentionally perpetuate sex, gender, and race biases

There has been increasing use of AI in healthcare to help in developments, diagnosing, and more personalised care. This surge has lead to two Stanford University faculty members calling for efforts to ensure this technology does not worsen existing health care disparities. 

In a new paper, the faculty discuss sex, gender, and race bias in medicine and how these biases could be perpetuated by AI devices. The authors have suggested different short and long-term approaches to prevent AI-related bias, such as changing policies at medical funding agencies and for publications to ensure the data collected for studies are diverse. 

“As we’re developing AI technologies for health care, we want to make sure these technologies have broad benefits for diverse demographics and populations,” said James Zou, assistant professor of biomedical data science and, by courtesy, of computer science and of electrical engineering at Stanford and co-author of the study.

The researchers suggested that the matter of bias will only become more important as personalised, precision medicine grows in the coming years. Personalised medicine, which is tailored to each patient based on factors such as their demographics and genetics, is vulnerable to injustice if AI medical devices cannot adequately account for individuals’ differences.

“We’re hoping to engage the AI biomedical community in preventing bias and creating equity in the initial design of research, rather than having to fix things after the fact,” said Londa Schiebinger, the John L. Hinds Professor in the History of Science in the School of Humanities and Sciences and senior author of the paper.

Addressing the bias

AI systems are only as good as the quality of their input data. If you can clean your training dataset from conscious and unconscious assumptions on race, gender, or other ideological concepts, you are able to build an AI system that makes unbiased data-driven decisions.

The study outlined challenges that can lead to bias and found they are fundamentally linked to how we design and collect the data used to train and evaluate the algorithms. Technology alone will not fix the issues; social problems that support structural inequality will have to be addressed. Researchers and educators can also do their part to develop education and technologies that strive toward social justice.

 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

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

 

Lower costs

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.

Deeper insights 

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. 
 

Intelligent automation 

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. 

Increased security 

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

 

Data privacy 

 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.

Connectivity concerns 

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.

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