Gender Legislative Index uses machine learning to identify discriminatory laws

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Reduce discrimination within the law.

Gender inequality can be imbedded in discriminatory laws, and until recently there has been no way to detect gender discrimination in the law.

Ramona Vijeyarasa, human rights lawyer and senior lecturer at the University of Technology Sydney has developed the Gender Legislative Index (GLI) to combat this problem, and close the gender inequality gap in Australia through law reform.

Vijeyarasa told Digital Nation Australia, “A lot of discrimination that women face is embedded in the law. And that may be in the form of a law that discriminates, for example, against single mothers, as opposed to non single mothers or a law that embeds inequality, such as Australia's paid parental leave scheme, which defines mothers as the primary carer, as opposed to both parents sharing that responsibility.”

The GLI uses AI and machine learning to evaluate whether a law will advance or hinder women’s rights based on criteria from international women’s rights law standards.

“The Gender Legislative Index uses a machine learning algorithm to give a law an overall score, because people do want to know that. They want to know, is this a good law? Is this a bad law? Which country performs best? But they want a way to show that that's underpinned by integrity. And that's what the machine learning algorithm offers,” she said.

“The machine learning is supposed to parallel human reasoning. The GLI algorithm operates as a series of ordered logical decisions based on the human evaluations, which flow to a final overall score for the law. So very much following a decision tree model.”

The unique aspect of the GLI is its ability to treat all laws the same, and therefore remove some of the human bias associated, she said.

Vijeyarasa used the index recently when the Australian government called for submissions for its workplace gender equality act.

“I was able to put forward the findings in the Gender Legislative Index, including the data from the machine learning to say, ‘These are great provisions in the law. This is also some of the factors in the law that need to be changed and it might be dragging it down. There's not enough transparency or accountability. Not enough organisations are brought within the scope of the law to be accountable.’”

The GLI is set to be used again soon as the government calls for revisions to the modern slavery act, she said.


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