5 Strengths and Weaknesses of Smart Case Weighting

Smart case weighting has some specific strengths compared to traditional case weighting. Most importantly:

• Smart case weighting allows us to adopt a more realistic approach. Traditional case weighting relies on the assumption that the proportional weights are exactly right, that it for example requires (on average) exactly 10 times as much work to complete a case type A case compared to a case type B case. Even with the best time studies or expert panel estimates we can never be sure it is exactly so. That is why it is better to allow for some uncertainty about weights.

• The relaxation of the requirement, that we have to be very precise about weights, may help to speed up the process of generating a case weighting system. One of the most serious obstacles for establishing a traditional case weighting system is the time and resources needed to determine and achieve consensus regarding the exact weights for each case type. Typically, this requires detailed time studies involving many, if not all, of the judges in the courts. In addition, consensus typically requires several meetings to set, assess and readjust weights. In most countries, the process for establishing a traditional weighted caseload model has spanned over several years.

• Smart case weighting has a conservative bias. This is especially beneficial when it comes to resource allocation because it is very important to ensure each court is given a fair assessment of its workload. A decision about reallocating staff is a serious one, and it is crucial to make certain that such reallocations only take place when there is a sound basis for knowing that they will in fact contribute to increase the overall efficiency and balance of the court system.

There are also some challenges associated with smart case weighting. Some of these challenges are generic (they are common for both traditional and smart case weighting), while other challenges are more specific to smart case weighting.

One of the most important generic challenges is the reliance on high-quality case statistics. If case statistics are erroneous, both traditional and smart case weighting will fail to provide accurate estimates for the optimal allocation of staff.

Another generic challenge is we have to rely on data from the past. We use past data for incoming cases to predict future workload. But sometimes the future may turn out to be very different from the past, or from what we expected.

Some specific challenges are related to the incorporation of uncertainty into the models. While conservative bias has been listed as a virtue of smart case weighting above, the models may of course also become too conservative, so that we fail to identify actual and permanent workload imbalances. The risk is only likely to occur if 1) we have a small number of courts; and/or 2) we have only few or weak assumptions about the case weights.

In addition, case weighting has often been used mainly as a tool to convince budgetary authorities about the need for more funds to the entire court system. While allowing for uncertainty appears to be a more honest approach to assessing needs, it may not always provide the results expenditure advocates are looking for.

The most important specific challenge when applying smart case weighting is however probably its reliance on complicated math. Becuase of that, it is more difficult to explain and apply than traditional case weighting. In these years complicated math - e.g. in the form of AI-algoritmhs - is however becoming more commonly used in many court systems anyway. So perhaps this will not be a major barrier in the future? In any case, this presentation has hopefully helped to illustrate what smart case weighting is about.