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Tag Archives for " machine learning "

How To Avoid Bias In Data Collection

Data collection is the most crucial part of machine learning models as the working of the model will completely depend on the data which we push as training.

Knowing what you really want to do with your data and more basically its purpose to serve your specific project is a very crucial part. You should develop a clear understanding of the data requirements before you take any further step of collecting data.

Full article: How To Avoid Bias In Data Collection

How to achieve digital governance?

Digital governance is corporate oversight of technologies that use personal or sensitive information, make autonomous decisions or exercise human-like responsibilities. The concept addresses disruptive technologies including artificial intelligence (AI), connected devices (IoT, cars, ubiquitous sensors, etc), and machine learning.

To establish digital governance programmes, companies must:

  1. first structure themselves accordingly,
  2. have a full picture of what they are doing,
  3. create an organisational culture that values fair digital practices.

Full article: Data Protection & Cybersecurity 2019 | Global Practice Guides | Chambers and Partners

How to address new privacy issues raised by artificial intelligence and machine learning

Artificial intelligence and machine learning present unique challenges for protecting the privacy of personal data.

For this reason, policymakers need to craft new national privacy legislation that accounts for the numerous limitations that scholars have identified in the notice and consent model of privacy that has guided privacy thinking for decades. The exacerbation of privacy externalities created by machine learning techniques is just one more reason regarding the need for new privacy rules.

Full article: How to address new privacy issues raised by artificial intelligence and machine learning

Artificial Intelligence: Privacy Promise or Peril?

Advanced algorithms, machine learning (ML), and artificial intelligence (AI) are appearing across digital and technology sectors from healthcare to financial institutions, and in contexts ranging from voice-activated digital assistants, to traffic routing, identifying at-risk students, and getting purchase recommendations on various online platforms.

Full article: Artificial Intelligence: Privacy Promise or Peril?

AI for Cybersecurity Is a Hot New Thing — and a Dangerous Gamble

Machine learning and artificial intelligence can help guard against cyberattacks, but hackers can foil security algorithms by targeting the data they train on and the warning flags they look for.

Read article: AI for Cybersecurity Is a Hot New Thing — and a Dangerous Gamble

The privacy pro’s guide to explainability in machine learning

With the GDPR’s implementation date looming, there has been much discussion about whether the regulation requires a “right to an explanation” from machine learning models.

Regardless of the regulation’s effects on machine learning, however, the practical implications of attempting to explain machine learning models presents significant difficulties.

Source: The privacy pro’s guide to explainability in machine learning

The privacy pro’s guide to explainability in machine learning

With the GDPR’s implementation date looming, there has been much discussion about whether the regulation requires a “right to an explanation” from machine learning models.

Regardless of the regulation’s effects on machine learning, however, the practical implications of attempting to explain machine learning models presents significant difficulties. These difficulties will become an increasing focus for privacy professionals as machine learning is deployed more and more throughout organizations in the future. The GDPR, in short, will not be the last law to address this issue.

Source: The privacy pro’s guide to explainability in machine learning

Machine learning as a service: Can privacy be taught?

Machine learning requires massive amounts of data to teach the model. But we’re often uploading that data to machine learning cloud services run by folks like Amazon and Google, where it might be exposed to malicious actors. Can we use machine-learning-as-service and protect privacy?

In a recent paper, Chiron: Privacy-preserving Machine Learning as a Service Tyler Hunt, of the University of Texas, and others, presents a system that preserves privacy while enabling the use of cloud MLaaS.

Source: Machine learning as a service: Can privacy be taught? | ZDNet

Unlock the Value: From Data Quality to Artificial Intelligence

Data quality, data privacy, and advanced technologies such as AI, machine learning, neural networks, and more, are of top concern to data analytics pros and IT managers.

A data analytics program can be an engine that fuels digital transformation projects and operations, helps you better engage with customers, and uncovers insights that lead to that next revenue stream. These programs have become of strategic value to organizations and essential components of digital transformation efforts.

Source: Unlock the Value: From Data Quality to Artificial Intelligence – InformationWeek

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